Tag: data visibility

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Digital Transformation In Retail: The Retail Apocalypse

Much like the hospitality industry, digital transformation in retail has been a huge driver of change.

One important fact is getting lost among all of the talk of “the retail apocalypse” and myriad stories about increasingly empty shopping malls: there’s a lot of money to be made in retail. In fact, the retail market was expected to grow by more than 3 percent in 2018, unemployment is low, and wages are at least stable.

In short, there’s money to be spent. Now, where are shoppers spending it?

Coming into 2019, consumers are in control when it comes to retail. Choices are abundant. According to Deloitte’s 2018 Retail, Wholesale and Distribution Industry Trends Outlook, “consumers have been conditioned to expect fast, convenient and effortless consumption.”

This is arguably the result of the degree of digital transformation in retail that we’ve seen in recent years.

If you want to survive in retail today, you need to make it easy on your customers. That means meeting their needs across channels, fulfilling orders quickly and accurately, offering competitive prices, and not sacrificing quality in the process.

Even in a world where Amazon has changed the retail game, Walmart just announced that it had its best holiday season in years. According to a recent Fortune article, “Walmart’s e-commerce sales rose 43 percent during the quarter, belying another myth: e-commerce and store sales are in competition with each other.”

Retail has always been a very fickle industry, with the right product mix and the right appeal to the right customers being crucial to success. But digital transformation in retail has seen the map change. You’re no longer competing with the store across the street; you’re competing with the store across the globe.

Digital Transformation In Retail

Retailers are putting every aspect of their businesses under scrutiny to help them remain relevant. Four areas in particular are getting a great deal of attention:

Customer experience: In today’s need-it-fast, need-it-now, need-it-right world, customers expect the ability to make purchases where they are, not where you are. That means via the Web, mobile devices or in a store. And all of the information about those orders needs to be tied together, so that if there is a problem, it can be resolved quickly via any channel.

Competitive differentiation: Appealing to retail customers used to mean appealing to all of your customers as one group or like-minded block. But customers are individuals, and today they can be targeted with personalized messaging and products that are likely to appeal to them, not to everyone.

Supply chain: Having the right products in the right place at the right time is part of the supply chain strategy. But moving them efficiently and cost effectively from any number of suppliers to warehouses and stores can make or break margins.

Partnerships: Among the smaller players in the retail space, partnerships with industry giants like Amazon can help reach a global audience that simply isn’t otherwise available and also reduce complexity. Larger players also recognize that partnerships can be mutually beneficial in the retail space.

Enabling each of these strategies is data – and lots of it. Data is the key to recognizing customers, personalizing experiences, making helpful recommendations, ensuring items are in stock, tracking deliveries and more. At its core, this is what digital transformation in retail seeks to achieve.

Digital Transformation in Retail – What’s the Risk?

But if data is the great enabler in retail, it’s also a huge risk – risk that the data is wrong, that it is old, and that it ends up in the hands of some person or entity that isn’t supposed to have it.

Danny Sandwell, director of product marketing for erwin, Inc., says retailers need to achieve a level of what he calls “data intelligence.” A little like business intelligence, Sandwell uses the term to mean that when someone in retail uses data to make a decision or power an experience or send a recommendation, they have the ability to find out anything they need about that data, including its source, age, who can access it, which applications use it, and more.

Given all of the data that flows into the modern retailer, this level of data intelligence requires a holistic, mature and well-planned data governance strategy. Data governance doesn’t just sit in the data warehouse, it’s woven into business processes and enterprise architecture to provide data visibility for fast, accurate decision-making, help keep data secure, identify problems early, and alert users to things that are working.

How important is clean, accurate, timely data in retail? Apply it to the four areas discussed above:

Customer experience:  If your data shows a lot of abandoned carts from mobile app users, then that’s an area to investigate, and good data will identify it.

Competitive differentiation: Are personalized offers increasing sales and creating customer loyalty? This is an important data point for marketing strategy.

Supply chain: Can a problem with quality be related to items shipping from a certain warehouse? Data will zero in on the location of the problem.

Partnerships: Are your partnerships helping grow other parts of your business and creating new customers? Or are your existing customers using partners in place of visiting your store? Data can tell you.

Try drawing these conclusions without data. You can’t. And even worse, try drawing them with inaccurate data and see what happens when a partnership that was creating customers is ended or mobile app purchases plummet after an ill-advised change to the experience.

If you want to focus on margins in retail, don’t forget this one: there is no margin for error.

Over the next few weeks, we’ll be looking closely at digital transformation examples in other sectors, including hospitality and government. Subscribe to to stay in the loop.

Data Management and Data Governance: Solving the Enterprise Data Dilemma

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erwin Expert Blog

Top 10 Reasons to Automate Data Mapping and Data Preparation

Data preparation is notorious for being the most time-consuming area of data management. It’s also expensive.

“Surveys show the vast majority of time is spent on this repetitive task, with some estimates showing it takes up as much as 80% of a data professional’s time,” according to Information Week. And a Trifacta study notes that overreliance on IT resources for data preparation costs organizations billions.

The power of collecting your data can come in a variety of forms, but most often in IT shops around the world, it comes in a spreadsheet, or rather a collection of spreadsheets often numbering in the hundreds or thousands.

Most organizations, especially those competing in the digital economy, don’t have enough time or money for data management using manual processes. And outsourcing is also expensive, with inevitable delays because these vendors are dependent on manual processes too.

Automate Data Mapping

Taking the Time and Pain Out of Data Preparation: 10 Reasons to Automate Data Preparation/Data Mapping

  1. Governance and Infrastructure

Data governance and a strong IT infrastructure are critical in the valuation, creation, storage, use, archival and deletion of data. Beyond the simple ability to know where the data came from and whether or not it can be trusted, there is an element of statutory reporting and compliance that often requires a knowledge of how that same data (known or unknown, governed or not) has changed over time.

A design platform that allows for insights like data lineage, impact analysis, full history capture, and other data management features can provide a central hub from which everything can be learned and discovered about the data – whether a data lake, a data vault, or a traditional warehouse.

  1. Eliminating Human Error

In the traditional data management organization, excel spreadsheets are used to manage the incoming data design, or what is known as the “pre-ETL” mapping documentation – this does not lend to any sort of visibility or auditability. In fact, each unit of work represented in these ‘mapping documents’ becomes an independent variable in the overall system development lifecycle, and therefore nearly impossible to learn from much less standardize.

The key to creating accuracy and integrity in any exercise is to eliminate the opportunity for human error – which does not mean eliminating humans from the process but incorporating the right tools to reduce the likelihood of error as the human beings apply their thought processes to the work.  

  1. Completeness

The ability to scan and import from a broad range of sources and formats, as well as automated change tracking, means that you will always be able to import your data from wherever it lives and track all of the changes to that data over time.

  1. Adaptability

Centralized design, immediate lineage and impact analysis, and change activity logging means that you will always have the answer readily available, or a few clicks away.  Subsets of data can be identified and generated via predefined templates, generic designs generated from standard mapping documents, and pushed via ETL process for faster processing via automation templates.

  1. Accuracy

Out-of-the-box capabilities to map your data from source to report, make reconciliation and validation a snap, with auditability and traceability built-in.  Build a full array of validation rules that can be cross checked with the design mappings in a centralized repository.

  1. Timeliness

The ability to be agile and reactive is important – being good at being reactive doesn’t sound like a quality that deserves a pat on the back, but in the case of regulatory requirements, it is paramount.

  1. Comprehensiveness

Access to all of the underlying metadata, source-to-report design mappings, source and target repositories, you have the power to create reports within your reporting layer that have a traceable origin and can be easily explained to both IT, business, and regulatory stakeholders.

  1. Clarity

The requirements inform the design, the design platform puts those to action, and the reporting structures are fed the right data to create the right information at the right time via nearly any reporting platform, whether mainstream commercial or homegrown.

  1. Frequency

Adaptation is the key to meeting any frequency interval. Centralized designs, automated ETL patterns that feed your database schemas and reporting structures will allow for cyclical changes to be made and implemented in half the time of using conventional means. Getting beyond the spreadsheet, enabling pattern-based ETL, and schema population are ways to ensure you will be ready, whenever the need arises to show an audit trail of the change process and clearly articulate who did what and when through the system development lifecycle.

  1. Business-Friendly

A user interface designed to be business-friendly means there’s no need to be a data integration specialist to review the common practices outlined and “passively enforced” throughout the tool. Once a process is defined, rules implemented, and templates established, there is little opportunity for error or deviation from the overall process. A diverse set of role-based security options means that everyone can collaborate, learn and audit while maintaining the integrity of the underlying process components.

Faster, More Accurate Analysis with Fewer People

What if you could get more accurate data preparation 50% faster and double your analysis with less people?

erwin Mapping Manager (MM) is a patented solution that automates data mapping throughout the enterprise data integration lifecycle, providing data visibility, lineage and governance – freeing up that 80% of a data professional’s time to put that data to work.

With erwin MM, data integration engineers can design and reverse-engineer the movement of data implemented as ETL/ELT operations and stored procedures, building mappings between source and target data assets and designing the transformation logic between them. These designs then can be exported to most ETL and data asset technologies for implementation.

erwin MM is 100% metadata-driven and used to define and drive standards across enterprise integration projects, enable data and process audits, improve data quality, streamline downstream work flows, increase productivity (especially over geographically dispersed teams) and give project teams, IT leadership and management visibility into the ‘real’ status of integration and ETL migration projects.

If an automated data preparation/mapping solution sounds good to you, please check out erwin MM here.

Solving the Enterprise Data Dilemma

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erwin Expert Blog

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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