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Data Governance Helps Build a Solid Foundation for Analytics

If your business is like many, it’s heavily invested in analytics. We’re living in a data-driven world. Data drives the recommendations we get from retailers, the coupons we get from grocers, and the decisions behind the products and services we’ll build and support at work.

None of the insights we draw from data are possible without analytics. We routinely slice, dice, measure and (try to) predict almost everything today because data is available to be analyzed. In theory, all this analysis should be helping the business. It should ensure we’re creating the right products and services, marketing them to the right people, and charging the right price. It should build a loyal base of customers who become brand ambassadors, amplifying existing marketing efforts to fuel more sales.

We hope all these things happen because all this analysis is expensive. It’s not just the cost of software licenses for the analytics software, but it’s also the people. Estimates for the average salary of data scientists, for example, can be upwards of $118,000 (Glassdoor) to $131,000 (Indeed). Many businesses also are exploring or already use next-generation analytics technology like predictive analytics or analytics supported by artificial intelligence or machine learning, which require even more investment.

If the underlying data your business is analyzing is bad, you’re throwing all this investment away. There’s a saying that scares everyone involved in analytics today: “Garbage in, garbage out.” When bad data is used to drive your strategic and operational decisions, your bad data suddenly becomes a huge problem for the business.

The goal, when it comes to the data you feed your analytics platforms, is what’s often referred to as the “single source of truth,” otherwise known as the data you can trust to analyze and create conclusions that drive your business forward.

“One source of truth means serving up consistent, high-quality data,” says Danny Sandwell, director of product marketing at erwin, Inc.

Despite all of the talk in the industry about data and analytics in recent years, many businesses still fail to reap the rewards of their analytics investments. In fact, Gartner reports that more than 60 percent of data and analytics projects fail. As with any software deployment, there are a number of reasons these projects don’t turn out the way they were planned. Among analytics, however, bad data can turn even a smooth deployment on the technology side into a disaster for the business.

What is bad data? It’s data that isn’t helping your business make the right decisions because it is:

  • Poor quality
  • Misunderstood
  • Incomplete
  • Misused

How Data Governance Helps Organizations Improve Their Analytics

More than one-quarter of the respondents to a November 2017 survey by erwin Inc. and UBM said analytics was one of the factors driving their data governance initiatives.

Reputation Management - What's Driving Data Governance

Data governance helps businesses understand what data they have, how good it is, where it is, and how it’s used. A lot of people are talking about data governance today, and some are putting that talk into action. The erwin-UBM survey found that 52 percent of respondents say data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) say they recognize the value of data to their organizations but don’t have a formal governance strategy.

Data-driven Analytics: How Important is Data Governance

When data governance helps your organization develop high-quality data with demonstrated value, your IT organizations can build better analytics platforms for the business. Data governance helps enable self-service, which is an important part of analytics for many businesses today because it puts the power of data and analysis into the hands of the people who use the data on a daily basis. A well-functioning data governance program creates that single version of the truth by helping IT organizations identify and present the right data to users and eliminate confusion about the source or quality of the data.

Data governance also enables a system of best practices, subject matter experts, and collaboration that are the hallmarks of today’s analytics-driven businesses.

Like analytics, many early attempts at instituting data governance failed to deliver the expected results. They were narrowly focused, and their advocates often had difficulty articulating the value of data governance to the organization, which made it difficult to secure budget. Some organizations even viewed data governance as part of data security, securing their data to the point where the people who wanted to use it had trouble getting access.

Issues of ownership also hurt early data governance efforts, as IT and the business couldn’t agree on which side was responsible for a process that affects both on a regular basis. Today, organizations are better equipped to resolve these issues of ownership because many are adopting a new corporate structure that recognizes how important data is to modern businesses. Roles like chief data officer (CDO), which increasingly sits on the business side, and the data protection officer (DPO), are more common than they were a few years ago.

A modern data governance strategy weaves itself into the business and its infrastructure. It is present in the enterprise architecture, the business processes, and it helps organizations better understand the relationships between data assets using techniques like visualization. Perhaps most important, a modern approach to data governance is ongoing because organizations and their data are constantly changing and transforming, so their approach to data governance needs to adjust as they go.

When it comes to analytics, data governance is the best way to ensure you’re using the right data to drive your strategic and operational decisions. It’s easier said than done, especially when you consider all the data that’s flowing into a modern organization and how you’re going to sort through it all to find the good, the bad, and the ugly. But once you do, you’re on the way to using analytics to draw conclusions you can trust.

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Data Plays Huge Role in Reputation Management

How much does your business invest in reputation management? It’s likely no one in the organization knows for sure because every interaction – in person, online or over the phone – can affect your firm’s reputation. The quality of the goods and services your organization provides, the training it gives employees, and the causes and initiatives it supports all can improve or worsen its reputation.

Reputation management has always been important to businesses, but because information flows so quickly and freely today, reputations are more fragile than ever. Bad news travels fast; often much faster than businesses can respond. It’s also incredibly hard to make bad news go away. Social media and search engines crushed the concept of the news cycle because they make it easy for information to circulate, even long after incidents have occurred.

One of the fastest ways to see your organization’s reputation suffer today is to lose or expose sensitive data. A study in the U.K. found that 86 percent of customers would not do business with a company that failed to protect its customers’ credit card data.

But data theft isn’t the only risk. Facebook may not have even violated its user agreement in the Cambridge Analytica scandal, but reputations have a funny way of rising and falling on perception, not just facts.

It’s estimated that Walmart, for example, spent $18 million in 2016 and 2017 on advertising for retrospective reputation management, after suffering from a perception the company was anti-worker, fixated on profits, and selling too many foreign-made products.

Perception is why companies publicize their efforts to be good corporate citizens, whether it means supporting charities or causes, or discussing sustainability initiatives that are aimed at protecting the environment.

When you are perceived as having a good reputation, a number of positive things happen. For starters, you can invest $18 million in your business and your customers, instead of spending it on ads you hope will change people’s perceptions of your company. But good reputation management also helps create happy, loyal customers who in turn become brand advocates spreading the word about your company.

Data permeates this entire process. Successful reputation management shows up in the data your business collects. Data also will help identify the brand ambassadors who are helping you sell your products and services.  When something goes wrong, the problem might first appear – and be resolved – thanks to data. But what data giveth, data can taketh away.

A big part of building and maintaining a good reputation today means avoiding missteps like those suffered by Facebook, Equifax, Uber, Yahoo, Wells Fargo and many others. Executives clearly grasp the importance of understanding and governing their organization’s data assets. More than three-quarters of the respondents to a November 2017 survey by erwin, Inc. and UBM said understanding and governing data assets is important or very important to their executives.

Reputation Management - How Important is DG

A strong data governance practice gives businesses the needed visibility into their data – what they’re collecting, why they’re collecting it, who can access it, where it’s stored, how it’s used, and more. This visibility can help protect reputations because knowing what you have, how it’s used, and where it is helps improve data protection.

Having visibility into your data also enables transparency, which works in two ways. Internally, transparency means being able to quickly and accurately answer questions posed by executives, auditors or regulators. Customer-facing transparency means businesses have a single view of their customers, so they can quickly solve problems, answer questions, and help align the products and services most relevant to customer needs.

Both types of transparency help manage an organization’s reputation. Businesses with a well-developed strategy for data governance are less likely to be caught off guard by a data breach months after the fact, and are better positioned to deliver the modern, personalized, omnichannel customer experience today’s consumers crave.

The connection between data governance and reputation is well understood. The erwin-UBM study found that 30 percent of organizations cite reputation management as the primary driver of their data governance initiative.

Reputation Management - What's Driving Data Governance

But data governance is more than protecting data (and by extension, your reputation). It is, when done well, a practice that permeates the organization. Integrating your data governance strategy with your enterprise architecture, for example, helps you define application capabilities and interdependencies within the context of your overall strategy. It also adds a layer of protection for data beyond your Level 1 security (the passwords, firewalls, etc., we know are vulnerable).

Data governance with a business process and analysis component helps enterprises clearly define, map and analyze their workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

For example, many businesses today are likely keeping too much data. A wave of accounting scandals in the early 2000s, most notably at Enron, led to regulations that included the need to preserve records and produce them in a timely manner. As a result, businesses started to store data like never before. Add to this new sources of data, like social media and sensors connected to the Internet of Things (IoT), and you have companies awash in data, paying (in some cases) more to store and protect it than it’s actually worth to their businesses.

When done well, data governance helps businesses make more informed decisions about data, such as whether the reward from the data they’re keeping is worth the risk and cost of storage.

“The further data gets from everyday use, it just sits on these little islands of risk,” says Danny Sandwell, director of product marketing for erwin.

All it takes is someone with bad intentions or improper training to airlift that data off the island and your firm’s reputation will crash and burn.

Alternatively, your organization can adopt data governance practices that will work to prevent data loss or misuse and enable faster remediation should a problem occur. Developing a reputation for “data responsibility” – from protecting data to transparency around its collection and use – is becoming a valuable differentiator. It’s entirely possible that as the number of data breaches and scandals continue to pile up, firms will start using their efforts toward data responsibility to enhance their reputation and appeal to customers, much in the way businesses talk about environmental sustainability initiatives.

A strong data governance foundation underpins data security and privacy. To learn more about how data governance will work for you, click here.

Examining the Data Trinity

 

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Avoiding Operational Disasters with a Process-Based Approach to Risk Management

Risk avoidance and risk management are hot topics that seem to govern decision-making – and with good reason. Risk comes with potentially massive operational, financial, reputational and legal repercussions, so it makes absolute sense to do everything possible to model it, understand it, analyse it and ultimately mitigate it.

But not all risk is created equal. Nothing illustrates this point better than recent research showing how much global financial institutions lost to different types of operational risk during the last six years. As shown in the chart below, they lost $210 billion between 2011 and 2016, with more than $180 billion of that amount attributed to execution, delivery and process management combined with clients, products and business practices.

So, major banks lost more money because of bad process management than all other risks combined. I’d argue that client, product and business practices, which comprise the largest risk category, essentially come down to process application and management as well.

Business Process-Based Approach to Risk Management

The Data Disconnect

Despite the actual statistics, we hear more about data/technology and compliance risk. While these are significant and justified concerns, financial institutions don’t seem to realize they are losing more money due to other types of risks.

Therefore, I want to remind them – and all of us – that managing operational risk is an ongoing initiative, which needs to include better risk analysis, documentation, process impact analysis and mitigation.

While dozens of methodologies and systems are available in today’s marketplace, they only focus on  or attempt to address the smaller, individual components of operational risk. However, all the risk categories listed above require an effective, practical and – most important – easy-to-implement system to address all the underlying components in a collaborative effort – not in isolation.

According to ORX, the largest operational risk association in the financial services sector, managing  and thereby reducing risk involves managing four different but interconnected layers: people, IT, organizational processes and regulations.

More and more organizations seem to believe that once IT embeds their applications with the necessary controls to meet regulatory requirements, then all is right with the world. But experience has shown that isn’t true. Without adapting the processes using the applications, training employees, and putting sufficient controls in place to ensure all regulatory elements are not only applied but applied correctly, then technical controls alone will ever be effective.

And many will argue that little can be done within an organization regarding regulations, but that’s not true either. While regulations are developed and passed by governments and other external regulatory bodies, what really matters is how organizations adopt those regulations and embed them into their culture and daily operations – which is where all the layers of risk management intersect.

Avoiding Heisenberg’s Uncertainty Principle in Risk Management

As part of his Nobel Prize-winning work, physicist and quantum mechanics pioneer Werner Heisenberg developed the eponymous uncertainty principle that asserts it is only possible to know either the position or movement of a particle but not both. This theory applies to many aspects of everyday life, including organizational operations. It’s difficult to know both an organization’s current state and where it’s headed, and every organization struggles with the same risk management question in this vein: how do we manage risk while also being agile enough to support growth?

ORX is clear that effective risk management requires implementing controls throughout the entire process ecosystem by integrating risk management into the organization’s very fabric. This means clearly defining roles and responsibilities, embedding process improvements, and regularly controlling process performance. Of course, the common thread here is more streamlined and controlled processes.

Make no mistake – effort is still required, but all the above is much simpler today. Thanks to new methodologies and comprehensive business process modeling systems, you can identify which risks are applicable, where they are most likely to occur, and who is responsible for managing them to reduce their probability and impact. Therefore, operational risk can be viewed and then addressed quickly and effectively.

In fact, erwin has worked with an increasing number of financial institutions launching process improvement, automation and management initiatives specifically designed to restructure their processes to promote flexibility as a growth driver without sacrificing traceability and control.

We can help you do the same, regardless of your industry.

To find out about how erwin can help in empowering your data-driven business initiatives, please click here.

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Why Data Governance and Business Process Management Must Be Linked

Data governance and business process management must be linked.

Following the boom in data-driven business data governance (DG) has taken the modern enterprise by storm, garnering the attention of both the business and technical realms with an explosion of methodologies, targeted systems and training courses. That’s because a major gap needs to be addressed.

But despite all the admonitions and cautionary tales, little attention has focused on what can literally make or break any data governance initiative, turning it from a springboard for competitive advantage to a recipe for waste, anger and ultimately failure. The two key pivot points on which success hinges are business process management (BPM) and enterprise architecture. This article focuses on the critical connections between data governance and business process management.

Based on a True Story: Data Governance Without Process Is Not Data Governance

The following is based on a true story about a global pharmaceutical company implementing a cloud-based, enterprise-wide CRM system with a third-party provider.

Given the system’s nature, the data it would process, and the scope of the deployment, data security and governance was front and center. There were countless meetings – some with more than 50 participants – with protocols sent, reviewed, adjusted and so on. In fact, more than half a dozen outside security companies and advisors (and yes, data governance experts) came in to help design the perfect data protection system around which the CRM system would be implemented.

The framework was truly mind-boggling: hundreds of security measures, dozens of different file management protocols, data security software appearing every step of the way.  Looking at it as an external observer, it appeared to be an ironclad net of absolute safety and effective governance.

But as the CRM implementation progressed, holes began to appear. They were small at first but quickly grew to the size of trucks, effectively rendering months of preparatory work pointless.

Detailed data transfer protocols were subverted daily by consultants and company employees who thought speed was more important than safety. Software locks and systems were overridden with passwords freely communicated through emails and even written on Post-It Notes. And a two-factor authentication principle was reduced to one person entering half a password, with a piece of paper taped over half the computer screen, while another person entered the other half of the password before a third person read the entire password and pressed enter.

While these examples of security holes might seem funny – in a sad way – when you read them here, they represent a $500,000 failure that potentially could lead to a multi-billion-dollar security breach.

Why? Because there were no simple, effective and clearly defined processes to govern the immense investment in security protocols and software to ensure employees would follow them and management could audit and control them. Furthermore, the organization failed to realize how complex this implementation was and that process changes would be paramount.

Both such failures could have been avoided if the organization had a simple system of managing, adjusting and monitoring its processes. More to the point, the implementation of the entire security and governance framework would have cost less and been completed in half the time. Furthermore, if a failure or breach were discovered, it would be easy to trace and correct.

Gartner Magic Quadrant

Data Governance Starts with BPM

In a rush to implement a data governance methodology and system, you can forget that a system must serve a process – and be governed/controlled by one.

To choose the correct system and implement it effectively and efficiently, you must know – in every detail – all the processes it will impact, how it will impact them, who needs to be involved and when. Do these questions sound familiar? They should because they are the same ones we ask in data governance. They involve impact analysis, ownership and accountability, control and traceability – all of which effectively documented and managed business processes enable.

Data sets are not important in and of themselves. Data sets become important in terms of how they are used, who uses them and what their use is – and all this information is described in the processes that generate, manipulate and use them. So, unless we know what those processes are, how can any data governance implementation be complete or successful?

Consider this scenario: We’ve perfectly captured our data lineage, so we know what our data sets mean, how they’re connected, and who’s responsible for them – not a simple task but a massive win for any organization.  Now a breach occurs. Will any of the above information tell us why it happened? Or where? No! It will tell us what else is affected and who can manage the data layer(s), but unless we find and address the process failure that led to the breach, it is guaranteed to happen again.

By knowing where data is used – the processes that use and manage it – we can quickly, even instantly, identify where a failure occurs. Starting with data lineage (meaning our forensic analysis starts from our data governance system), we can identify the source and destination processes and the associated impacts throughout the organization. We can know which processes need to change and how. We can anticipate the pending disruptions to our operations and, more to the point, the costs involved in mitigating and/or addressing them.

But knowing all the above requires that our processes – our essential and operational business architecture – be accurately captured and modelled. Instituting data governance without processes is like building a castle on sand.

Rethinking Business Process Management

Modern organizations need a simple and easy-to-use BPM system with easy access to all the operational layers across the organization – from high-level business architecture all the way down to data. Sure, most organizations already have various solutions here and there, some with claims of being able to provide a comprehensive picture. But chances are they don’t, so you probably need to rethink your approach.

Modern BPM ecosystems are flexible, adjustable, easy-to-use and can support multiple layers simultaneously, allowing users to start in their comfort zones and mature as they work toward the organization’s goals.

Processes need to be open and shared in a concise, consistent way so all parts of the organization can investigate, ask questions, and then add their feedback and information layers. In other words, processes need to be alive and central to the organization because only then will the use of data and data governance be truly effective.

Are you willing to think outside the traditional boxes or silos that your organization’s processes and data live in?

The erwin EDGE is one of the most comprehensive software platforms for managing an organization’s data governance and business process initiatives, as well as the whole data architecture. It allows natural, organic growth throughout the organization and the assimilation of data governance and business process management under the same platform provides a unique data governance experience because of its integrated, collaborative approach.

To learn more about erwin EDGE, and how data governance underpins and ensures data quality throughout the wider data management-suite, download our resource: Data Governance Is Everyone’s Business.

Data Governance is Everyone's Business

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Why Data Governance is the Key to Better Decision-Making

The ability to quickly collect vast amounts of data, analyze it, and then use what you’ve learned to help foster better decision-making is the dream of many a business executive. But like any number of things that can be summarized in a single sentence, it’s much harder to execute on such a vision than it might first appear.

According to Forrester, 74 percent of firms say they want to be “data-driven,” but only 29 percent say they are good at connecting analytics to action. Consider this: Forrester found that business satisfaction with analytics dropped by 21 percent between 2014 and 2015 – a period of great promise and great investment in Big Data. In other words, the more data businesses were collecting and mining, the less happy they were with their analytics.

A number of factors are potentially at play here, including the analytics software, the culture of the business, and the skill sets of the people using the data. But your analytics applications and the conclusions you draw from your analysis are only as good as the data that is collected and analyzed. Collecting, safeguarding and mining large amounts of data isn’t an inexpensive exercise, and as the saying goes, “garbage in, garbage out.”

“It’s a big investment and if people don’t trust data, they won’t use things like business intelligence tools because they won’t have faith in what they tell them,” says Danny Sandwell, director of product marketing at erwin, Inc.

Using data to inform business decisions is hardly new, of course. The modern idea of market research dates back to the 1920s, and ever since businesses have collected, analyzed and drawn conclusions from information they draw from customers or prospective customers.

The difference today, as you might expect, is the amount of data and how it’s collected. Data is generated by machines large and small, by people, and by old-fashioned market research. It enters today’s businesses from all angles, at lightning speed, and can, in many cases, be available for instant analysis.

As the volume and velocity of data increases, overload becomes a potential problem. Unless the business has a strategic plan for data governance, decisions around where the data is stored, who and what can access it, and how it can be used, becomes increasingly difficult to understand.

Not every business collects massive amounts of data like Facebook and Yahoo, but recent headlines demonstrate how those companies’ inability to govern data is harming their reputations and bottom lines. For Facebook, it was the revelation that the data of 87 million users was improperly obtained to influence the 2016 U. S. presidential election. For Yahoo, the U.S. Securities and Exchange Commission (SEC) levied a $35 million fine for failure to disclose a data breach in a timely manner.

In both the Facebook and Yahoo cases, the misuse or failure to protect data was one problem. Their inability to quickly quantify the scope of the problem and disclose the details made a big issue even worse – and kept it in the headlines even longer.

The issues of data security, data privacy and data governance may not be top of mind for some business users, but these issues manifest themselves in a number of ways that affect what they do on a daily basis. Think of it this way: somewhere in all of the data your organization collects, a piece of information that can support or refute a decision you’re about to make is likely there. Can you find it? Can you trust it?

If the answer to these questions is “no,” then it won’t be easy for your organization to make data-driven decisions.

Better Decision-Making - Data Governance

Powering Better Decision-Making with Data Governance

Nearly half (45 percent) of the respondents to a November 2017 survey by erwin and UBM said better decision-making was one of the factors driving their data governance initiatives.

Data governance helps businesses understand what data they have, how good it is, where it is, and how it’s used. A lot of people are talking about data governance today, and some are putting that talk into action. The erwin/UBM survey found that 52 percent of respondents say data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) say they recognize the value of data to their organization but don’t have a formal governance strategy.

Many early attempts at instituting data governance failed to deliver results. They were narrowly focused, and their proponents often had difficulty articulating the value of data governance to the organization, making it difficult to secure budget. Some organizations even understood data governance as a type of data security, locking up data so tightly that the people who wanted to use it to foster better decision-making had trouble getting access.

Issues of ownership also stymied early data governance efforts, as IT and the business couldn’t agree on which side was responsible for a process that affects both on a regular basis. Today, organizations are better equipped to resolve issues of ownership, thanks in large part to a new corporate structure that recognizes how important data is to modern businesses. Roles like chief data officer (CDO), which increasingly sits on the business side, and the data protection officer (DPO), are more common than they were a few years ago.

A modern data governance strategy works a lot like data itself – it permeates the business and its infrastructure. It is part of the enterprise architecture, the business processes, and it help organizations better understand the relationships between data assets using techniques like visualization. Perhaps most important, a modern approach to data governance is ongoing, because organizations and their data are constantly changing and transforming, so their approach to data governance can’t sit still.

As you might expect, better visibility into your data goes a long way toward using that data to make more informed decisions. There is, however, another advantage to the visibility offered by a holistic data governance strategy: it helps you better understand what you don’t know.

By helping businesses understand the areas where they can improve their data collection, data governance helps organizations continually work to create better data, which manifests itself in real business advantages, like better decision-making and top-notch customer experiences, all of which will help grow the business.

Michael Pastore is the Director, Content Services at QuinStreet B2B Tech. This content originally appeared as a sponsored post on http://www.eweek.com/.

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Benefits of Process: Why Modern Organizations Need Process-Based Engines

In the current data-driven business climate, the benefits of process and process-based strategy are more desirable to organizations than ever.

Industry regulations and competition traditionally have driven organizational change, but such “transformation” has rarely been comprehensive or truly transformative. Rather, organizational transformation has come in waves, forcing companies and their IT ecosystems to ride them as best as they can – sometimes their fortunes have risen, and sometimes they have waned.

The advent of Brexit and GDPR have again forced today’s organizations to confront external stimuli’s impact on their operations. The difference is that the modern, process-based enterprises can better anticipate these sorts of mandates, incorporate them into their strategic plans, and even leapfrog ahead of their requirements by initiating true internal transformation initiatives – ones based on effectively managed and well-documented business processes.

Shifting Attitudes

Traditional organizations focus almost exclusively on rigid structures, centralized management and accountability; concentrated knowledge; service mainly to external customers; and reactive, short-term strategy alignment driven mainly by massive-scale projects. This traditional approach results in large, unwieldy and primarily reactive organizations that rely either on legacy strengths or inertia for survival.

But as technology evolves and proliferates, more and more organizations are realizing they need to adjust their traditional thinking and subsequent actions, even if just slightly, to gain strategic advantage, reduce costs and retain market dominance. For example:

  • Structures are becoming more adaptable, allowing for greater flexibility and cost management. How is this possible and why now? Organizations are grasping that effective, well-managed and documented business processes should form their operational backbones.
  • Business units and the departments within them are becoming accountable not only for their own budgets but also on how well they achieve their goals. This is possible because their responsibilities and processes can be clearly defined, documented and then monitored to ensure their work is executed in a repeatable, predictable and measurable way.
  • Knowledge is now both centralized and distributed thanks to modern knowledge management systems. Central repositories and collaborative portals give everyone within the organization equal access to the data they need to do their jobs more effectively and efficiently.
  • And thanks to all the above, organizations can expand their focus from external customers to internal ones as well. By clearly identifying individual processes (and their cross-business handover points) and customer touchpoints, organizations can interact with any customer at the right point with the most appropriate resources.

If business drivers are connected to processes with appropriate accountability, they become measurable in dimensions never before possible. Such elements as customer-journey quality and cost, process-delivery efficiency and even bottom-up cost aggregation can be captured. Strategic decision-making then becomes infinitely practical and forward-looking.

With this interconnected process – and information – based ecosystem, management can perform accurate and far-reaching impact analyses, test alternate scenarios, and evaluate their costs and implementation possibilities (and difficulties) to make decisions with full knowledge of their implications. Organizational departments can provide real-time feedback on designs and projects, turning theoretical designs into practical plans with buy-in at the right levels.

Benefits of Process

As stated above, one of the key benefits of process and a process-based organizational engine is that organizations should be able to better handle outside pressures, such as new regulations, if they are – or are becoming – truly process-based. Because once processes (and their encompassing business architecture) become central to the organization, a wide array of things become simpler, faster and cheaper.

The benefits of process don’t stop there either. Application design – the holy grail or black hole of budgetary spending and project management, depending on your point of view – is streamlined, with requirements clearly gathered and managed in perfect correspondence to the processes they serve and with the data they manage clearly documented and communicated to the developers. Testing occurs against real-life scenarios by the responsible parties as documented by the process owners – a drastic departure from the more traditional approaches in which the responsibility fell to designated, usually technical application owners.

Finally – and most important – data governance is no longer the isolated domain of data architects but central to the everyday processes that make an organization tick. As processes have stakeholders who use information – data – the roles of technical owners and data stewards become integral to ensuring processes operate efficiently, effectively and – above all – without interruptions. On the other side of this coin, data owners and data stewards no longer operate in their own worlds, distant from the processes their data supports.

Seizing a Process-Based Future

Process is a key axis along which the modern organization must operate. Data governance is another, with cost management becoming a third driver for the enterprise machine. But as we all know, it takes more than stable connecting rods to make an engine work – it needs cogs and wheels, belts and multiple power sources, all working together.

In the traditional organization, people are the internal mechanics. But one can’t escape visions of Charlie Chaplin’s Modern Times worker hopelessly entangled in the machine on which he was working. That’s why, these days, powerful and flexible workflow engines provide much-needed automation for greater visibility plus more power, stability and quality – all the things a machine needs to operate as required/designed.

Advanced process management systems are becoming essential, not optional. And while not as sexy or attention-grabbing as other technologies, they provide the power to drive an organization toward its goals quickly, cost-effectively and efficiently.

To learn how erwin can empower a modern, process-based organization, please click here.

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Data Governance Tackles the Top Three Reasons for Bad Data

In modern, data-driven busienss, it’s integral that organizations understand the reasons for bad data and how best to address them. Data has revolutionized how organizations operate, from customer relationships to strategic decision-making and everything in between. And with more emphasis on automation and artificial intelligence, the need for data/digital trust also has risen. Even minor errors in an organization’s data can cause massive headaches because the inaccuracies don’t involve just one corrupt data unit.

Inaccurate or “bad” data also affects relationships to other units of data, making the business context difficult or impossible to determine. For example, are data units tagged according to their sensitivity [i.e., personally identifiable information subject to the General Data Protection Regulation (GDPR)], and is data ownership and lineage discernable (i.e., who has access, where did it originate)?

Relying on inaccurate data will hamper decisions, decrease productivity, and yield suboptimal results. Given these risks, organizations must increase their data’s integrity. But how?

Integrated Data Governance

Modern, data-driven organizations are essentially data production lines. And like physical production lines, their associated systems and processes must run smoothly to produce the desired results. Sound data governance provides the framework to address data quality at its source, ensuring any data recorded and stored is done so correctly, securely and in line with organizational requirements. But it needs to integrate all the data disciplines.

By integrating data governance with enterprise architecture, businesses can define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritize technology investments so they align with business goals and strategies to produce the desired outcomes. A business process and analysis component enables an organization to clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

And data modeling remains the best way to design and deploy new relational databases with high-quality data sources and support application development. Being able to cost-effectively and efficiently discover, visualize and analyze “any data” from “anywhere” underpins large-scale data integration, master data management, Big Data and business intelligence/analytics with the ability to synthesize, standardize and store data sources from a single design, as well as reuse artifacts across projects.

Let’s look at some of the main reasons for bad data and how data governance helps confront these issues …

Reasons for Bad Data

Reasons for Bad Data: Data Entry

The concept of “garbage in, garbage out” explains the most common cause of inaccurate data: mistakes made at data entry. While this concept is easy to understand, totally eliminating errors isn’t feasible so organizations need standards and systems to limit the extent of their damage.

With the right data governance approach, organizations can ensure the right people aren’t left out of the cataloging process, so the right context is applied. Plus you can ensure critical fields are not left blank, so data is recorded with as much context as possible.

With the business process integration discussed above, you’ll also have a single metadata repository.

All of this ensures sensitive data doesn’t fall through the cracks.

Reasons for Bad Data: Data Migration

Data migration is another key reason for bad data. Modern organizations often juggle a plethora of data systems that process data from an abundance of disparate sources, creating a melting pot for potential issues as data moves through the pipeline, from tool to tool and system to system.

The solution is to introduce a predetermined standard of accuracy through a centralized metadata repository with data governance at the helm. In essence, metadata describes data about data, ensuring that no matter where data is in relation to the pipeline, it still has the necessary context to be deciphered, analyzed and then used strategically.

The potential fallout of using inaccurate data has become even more severe with the GDPR’s implementation. A simple case of tagging and subsequently storing personally identifiable information incorrectly could lead to a serious breach in compliance and significant fines.

Such fines must be considered along with the costs resulting from any PR fallout.

Reasons for Bad Data: Data Integration

The proliferation of data sources, types, and stores increases the challenge of combining data into meaningful, valuable information. While companies are investing heavily in initiatives to increase the amount of data at their disposal, most information workers are spending more time finding the data they need rather than putting it to work, according to Database Trends and Applications (DBTA). erwin is co-sponsoring a DBTA webinar on this topic on July 17. To register, click here.

The need for faster and smarter data integration capabilities is growing. At the same time, to deliver business value, people need information they can trust to act on, so balancing governance is absolutely critical, especially with new regulations.

Organizations often invest heavily in individual software development tools for managing projects, requirements, designs, development, testing, deployment, releases, etc. Tools lacking inter-operability often result in cumbersome manual processes and heavy time investments to synchronize data or processes between these disparate tools.

Data integration combines data from several various sources into a unified view, making it more actionable and valuable to those accessing it.

Getting the Data Governance “EDGE”

The benefits of integrated data governance discussed above won’t be realized if it is isolated within IT with no input from other stakeholders, the day-to-day data users – from sales and customer service to the C-suite. Every data citizen has DG roles and responsibilities to ensure data units have context, meaning they are labeled, cataloged and secured correctly so they can be analyzed and used properly. In other words, the data can be trusted.

Once an organization understands that IT and the business are both responsible for data, it can develop comprehensive, holistic data governance capable of:

  • Reaching every stakeholder in the process
  • Providing a platform for understanding and governing trusted data assets
  • Delivering the greatest benefit from data wherever it lives, while minimizing risk
  • Helping users understand the impact of changes made to a specific data element across the enterprise.

To reduce the risks of and tackle the reasons for bad data and realize larger organizational objectives, organizations must make data governance everyone’s business.

To learn more about the collaborative approach to data governance and how it helps compliance in addition to adding value and reducing costs, get the free e-book here.

Data governance is everyone's business

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Digital and Business Transformation Starts with Business Processes

The constantly evolving business landscape means digital and business transformation efforts must be made with continuous improvement in mind.

For Southern California Edison (SCE), detailed and comprehensive business process (BP) modeling is the only way to achieve continuous improvement. And because continuous improvement is one of SCE’s key corporate values, the company has chosen to rely on an efficient and effective BP management ecosystem to ensure success.

In today’s constantly changing environment, the old adage of change being the only constant holds truer than ever before, and it is this realization that sets SCE apart. Working from the base up with a focus on developing an accurate business architecture framework, combined with comprehensive information collateral, SCE can support targeted transformation initiatives along any axis the company requires.

Digital & Business Transformation

Business Architecture and Process Modeling

Planning and working toward a flexible, responsive and adaptable future is no longer enough – the modern organization must be able to visualize not only the end state (the infamous and so-elusive “to-be”) but also perform detailed and comprehensive impact analysis on each scenario, often in real time.  This analysis also needs to span multiple departments, extending beyond business and process architecture to IT, compliance and even HR and legal.

The ability of process owners to provide this information to management is central to ensuring the success of any transformation initiative. And new requirements and initiatives need to be managed in new ways. Digital and business transformation is about being able to do three things at the same time, all working toward the same goals:

  • Collect, document and analyze requirements
  • Establish all information layers impacted by the requirements
  • Develop and test the impact of multiple alternative scenarios

Comprehensive business process modeling underpins all of the above, providing the central information axis around which initiatives are scoped, evaluated, planned, implemented and ultimately managed.

Because of its central role, business process modeling must expand to modeling information from other layers within the organization, including:

  • System and application usage information
  • Supporting and reference documentation
  • Compliance, project and initiative information
  • Data usage

All these information layers must be captured and modeled at the appropriate levels, then connected to form a comprehensive information ecosystem that enables parts of the organization running transformation and other initiatives to instantly access and leverage it for decision-making, simulation and scenario evaluation, and planning, management and maintenance.

Breaking with Tradition

Traditionally, digital and business transformation initiatives relied almost exclusively on human knowledge and experience regarding processes, procedures, how things worked, and how they fit together to provide a comprehensive and accurate framework. Today, technology can aggregate and manage all this information – and more – in a structured, organized and easily accessible way.

Business architecture extends beyond simple modeling; it also incorporates automation to reduce manual effort, remove potential for error, and guarantee effective data governance – with visibility from strategy all the way down to data entry and the ability to trace and manage data lineage. It requires robotics to cross-reference mass amounts of information, never before integrated to support effective decision-making.

The above are not options that are “nice to have,” but rather necessary gateways to taking business process management into the future. And the only way to leverage them is through systemic, organized and comprehensive business architecture modeling and analysis. As Ryan Maddox, Process Improvement Manager at SCE explained, “[While] we could have generated the right procedures over time, we wouldn’t have had the analysis and simulation to make fully informed decisions or trained the right people and given them access to the correct information.”

Therefore, business architecture and process modeling are no longer a necessary evil. They are critical success factors to any digital or business transformation journey.

Focusing on Possibilities, Not Difficulties

Experts confirm the need to rethink and revise business processes to incorporate more digital automation. Forrester notes in a recent report, The Growing Importance of Process to Digital Transformation, that the changes in how business is conducted are driving the push “to reframe organizational operational processes around digital transformation efforts.” In a dramatic illustration of the need to move in this direction, the research firm writes that “business leaders are looking to use process as a competitive weapon.”

If a company hasn’t done a good job of documenting its processes, it can’t realize a future in which digital transformation is part of everyday operations. It’s never too late to start, though. In a fast-moving and pressure cooker business environment, companies need to implement business process models that make it possible to visually and analytically represent the steps that will add value to the company – either around internal operations or external ones, such as product or service delivery.

erwin BP, part of the erwin EDGE Platform, enables effective business architecture and process modeling. With it, any transformation initiative becomes a simple, streamlined exercise to support distributed information capture and management, object-oriented modeling, simulation and collaboration.

To find out about how erwin can help in empowering your transformation initiatives, please click here.

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The Connection Between Business Process Modeling and Standard Operating Procedures

We began a new blog series last week on business process (BP) modeling and its role within the enterprise. This week’s focus is on the connection between business process modeling and standard operating procedures. Specifically, using BP tools to help organizations streamline how they manage their standard operating procedures (SOPs).

Standard Operating Procedures: A New Approach to Organizing SOP Information

Manually maintaining the standard operating procedures that inform business processes can be a monster of a task. In most industries, SOPs typically are documented in multiple Word or Excel files.

In a process-centric world, heavy lifting is involved when an organization requires a change to an end-to-end process: Each SOP affected by the change may be associated with dozens or even hundreds of steps that exist between the start and conclusion of the process – and the alteration must be made to all of them wherever they occur.

You can imagine the significant man hours that go into wading through a sea of documents to discover and amend relevant SOPs and communicate these business process-related changes across the organization. And you can guess at the toll on productivity and efficiency that the business experiences as a result.

Companies that are eager to embrace business process optimization are keen to have a better approach to organizing SOP information to improve transparency and insight for speedier and more effective change management.

There’s another benefit to be realized from taking a new approach to SOP knowledge management, as well. With better organization comes an increased ability to convey information about current and changed standard operating procedures; companies can offer on-the-fly access to standard practices to teams across the enterprise.

That consistent and easily obtained business process information can help employees innovate, sharing ideas about additional improvements and innovations that could be made to standard operating procedures. It could also save them the time they might otherwise spend on “reinventing the wheel” for SOPs that already exist but that they don’t know about.

Balfour Beatty Construction, the fourth largest general builder in the U.S., saw big results when it standardized and transformed its process documentation, giving workers access to corporate SOPs from any location on almost any device.

As a construction company, keeping field workers out of danger is a major issue, and providing these employees with immediate information about how to accomplish a multi-step business process – such as clearing a site – can promote their safety. Among benefits it saw were a 5% gain in productivity and a reduction in training time for new employees who were now able to tap directly into SOP data.

Business Process Modeling & Standard Operating Procedures

Using Business Process Modeling to Transform SOP Management

How does a company transform manual SOP documentation to more effectively support change management as part of business process optimization? It’s key to adopt business process (BP) modeling and management software to create and store SOP documentation in a single repository, tying them to the processes they interact with for faster discovery and easier maintenance.

Organizations that move to this methodology, for example, will have the advantage of only needing to change an affected SOP in that one repository; the change automatically will propagate to all related processes and procedures.

In effect, the right BP tool automatically generates new SOPs with the necessary updated information.

Such a tool is also suitable for use in conjunction with controlled document repositories that are typically required in heavily regulated industries, such as pharmaceuticals, financial services and healthcare, as part of satisfying compliance mandates. All SOP documentation already is stored in the same repository, rather than scattered across files.

But a business process diagramming and modeling solution comes in handy in these cases by providing a web-based front-end that exposes high-end processes and how they map to related SOPs. This helps users better navigate them to institute and maintain changes and to access job-related procedure information.

To find out about how erwin can streamline SOP document management to positively impact costs, workloads and user benefits, please click here.

In our next blog, we’ll look at how business process modeling strengthens digital transformation initiatives.

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Business Process Modeling and Its Role Within the Enterprise

To achieve its objectives, an organization must have a complete understanding of its processes. Therefore, business process design and analysis are key to defining how a business operates and ensures employees understand and are accountable for carrying out their responsibilities.

Understanding system interactions, business processes and organizational hierarchies creates alignment, with everyone pulling in the same direction, and supports informed decision-making for optimal results and continuous improvement.

Those organizations operating in industries in which quality, health, safety and environmental issues are constant concerns must be even more in tune with their complexities. After all, revenue and risk are inextricably linked.

What Is Business Process Modeling and Why Does It Matter?

A business process is “an activity or set of activities that will accomplish a specific organizational goal,” as defined by TechTarget. Business process modeling “links business strategy to IT systems development to ensure business value,” according to Gartner.

The research firm goes on to explain that it “combines process/workflow, functional, organizational and data/resource views with underlying metrics, such as costs, cycle times and responsibilities, you establish a foundation for analyzing value chains, activity-based costs, bottlenecks, critical paths and inefficiencies.”

To clearly document, define, map and analyze workflows and build models to drive process improvement and therefore business transformation, you’ll need to invest in a business process (BP) modeling solution.

Only then will you be able to determine where cross-departmental and intra-system process chains break down, as well as identify business practices susceptible to the greatest security, compliance, standards or other risks and where controls and audits are most needed to mitigate exposures.

Companies that maintain accurate BP models also are well-positioned to analyze and optimize end-to-end process threads that help accomplish such strategic business objectives as improving customer journeys and maximizing employee retention. You also can slice and dice models in multiple other ways, including to improve collaboration and efficiency.

Useful change only comes from evaluating process models, spotting sub-optimalities, and taking corrective actions. Business process modeling is also critical to data governance, helping organizations understand their data assets in the context of where their data is and how it’s used in various processes. Then you can drive data opportunities, like increasing revenue, and limit data risks, such as avoiding regulatory and compliance gaffes.

How to Do Business Process Modeling

Business process modeling software creates the documentation and graphical roadmap of how a business works today, detailing the tasks, responsible parties and data elements involved in processes and the interactions that occur across systems, procedures and organizational hierarchies. That knowledge, in turn, prepares the organization for tomorrow’s changes.

Effective BP technology will assist your business in documenting, managing and communicating your business processes in a structured manner that drives value and reduces risks.

It should enable you to:

  • Develop and capture multiple artefacts in a repository to support business-centric objectives
  • Support process improvement methodologies that boost critical capabilities
  • Identify gaps in process documentation to retain internal mastery over core activities
  • Reduce maintenance costs and increase employee access to critical knowledge
  • Incorporate any data from any location into business process models

In addition, a business process modeling solution should work in conjunction with the other data management domains (i.e., enterprise architecture, data modeling and data governance) to provide data clarity across all organizational roles and goals.

Data Governance, Data Modeling, Enterprise Architecture, Business Process - erwin EDGE

Business Process Modeling and Enterprise Data Management

Data isn’t just for “the data people.” To survive and thrive in the digital age, among the likes of Amazon, Airbnb, Netflix and Uber that have transformed their respective industries, organizations must extend the use, understanding and trust of their data everyday across every business function – from the C-level to the front line.

A common source of data leveraged by business process personnel, enterprise architects, data stewards and others encourages a greater understanding of how different line-of-business operations work together as a single unit. Links to data terms and categories contained within a centralized business glossary let enterprises eliminate ambiguity in process and policy procedure documents.

Integrated business models based on a sole source of truth also offer different views for different stakeholders based on their needs, while tight interconnection with enterprise architecture joins Process, Organization, Location, Data, Applications, and Technology (POLDAT) assets to explanatory models that support informed plans for change.

Seamless integration of business process models with enterprise architecture, data modeling and data governance reveals the interdependence between the workforce, the processes they perform, the actively governed assets they interact with and their importance to the business.

Then everyone is invested in and accountable for data, the fuel for the modern enterprise.

To learn more about business process modeling and its role within data-driven business transformation, click here.

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