Tag: big data

Categories
erwin Expert Blog

How Digital Transformation is Changing the World

Technological revolutions of the past usually came with a bang. However, that’s not the case today as industry quietly transitions systems and processes to incorporate more advanced technology. Still, the outcomes tend to be dramatic with market disruptions and societal advances.

With the rise of the IoT and data – or Big Data to be precise – various industries now operate at increased levels of efficiency and productivity.

This blog post looks at some of the sectors undergoing digital transformation and the impacts.

Digital Transformation in Manufacturing and Retail

Due to the availability of digital tools and automation, manufacturers now have more streamlined and efficient operations without sacrificing quality.

Automobile makers are excellent examples in this regard, with their production plants fabricating parts that have meticulous details and measurements – a process achievable consistently and with less errors through the use of machines and computers.

Retail is another sector that’s progressing rapidly through digital transformation. For instance, the recent merger of Amazon and Whole Foods gives consumers easier and quicker access to products through a series of developments. Entire sections of various items have been added to their online shopping portals.

Retail spaces are being upgraded with interactive and/or virtual displays, giving customers new ways to test items before purchasing. The retail firm also launched its first Amazon Go store recently, which implements a ‘check-out free’ system – buyers simply get what they need and sensors embedded in the products signal the system to charge the users’ accounts as they walk out.

Digital Transformation in Transportation

digital transformation transportation

Uber is the perfect example of effective digital transformation in transportation. Hailing a ride is the underlying principle, but passengers can procure transport service easier, faster and at reasonable fares.

Of course, the transition didn’t stop there. Some companies are working to make large-scale formats available, such as shuttles and car-sharing, which you could think of as the Airbnb of the transportation sector.

Digital Transformation in Entertainment and Media

Forbes says the entertainment industry is becoming the testing ground for more digital content from retailers. For example, corporations that typically sell consumer packaged goods are venturing into original content and “actually producing edgy original series.”

PepsiCo is at the forefront of this next frontier, aiming to establish a greater presence in the age of Netflix, YouTube and other fresh streaming content channels.

The F&B giant even constructed its own massive studio that can relay real-time content to the entire globe.

Digital Transformation in Logistics and Supply Chains

In terms of logistics, ‘blockchain’ technology is revolutionizing entire processes that bring products from suppliers to consumers. For instance, the world’s leading shipping company, Maersk, recently conducted a test to track its cargo with blockchain, according to Fortune.

The key takeaway here is that each container was monitored remotely through digital connections protected by cryptographic signatures.

Digitalization is applied on the part of employees as well, specifically on vehicles that deliver cargo. As of last year, the U.S. requires fleet operators to use electronic logging devices that Fleetmatics specified as devices used mainly to track hours of service.

They’re given until the end of 2017 to comply. As such, truckers now have a digital tool for recording and receiving data, saving time and resources in regard to paperwork.

Digital Transformation in Finance and Banking

The finance and banking sector was quick to adopt mobile technology. ‘Digital finance’ is giving account holders the power to conduct transactions through handheld devices.

Emerging economies benefit greatly from this trend, with many financial institutions embracing the change from traditional to digital accounts. Moreover, mobility is being used in other related processes, such as loan applications, tax collection and fund allocation.

Digital Transformation in Medicine and Healthcare

Digital transformation medicine

Digital equipment and devices are taking over healthcare. Dr. GSK Velu, who contributes to the Economic Times’ Healthworld, even went as far as to say that the human being is now ‘digitized.’

Vital signs and other medical data now can be monitored even when the physician and patient are not in the same room. Surgery also can be performed with the surgeon a world away from the operating table.

Digital Transformation in Government

Several government applications and processes are also undergoing digital transformation. In many cases, such processes are already conducted digitally today. And to further improve such capabilities, Forbes relayed that nations like the U.S. are deploying AI support in select tasks.

Helpdesks and contact centers have the assistance of chatbots, and more advanced cybertechnologies are used for sustainability as well as national security.

Despite this, the U.S. Bureau of Labor Statistics projected that there will be minimal loss of government jobs within the foreseeable future, as most of the manpower will be transferred to more public-centric services. It’s a win-win situation for both government and its citizens.

Digital Transformation is Everywhere

It’s clear that data makes the world go ‘round, and more developments are to come. As data collection, flow and management are further enhanced, innovations such as smart cities and connected cars will be fully realized. Futurism suggests that consumers will even begin to profit from the data they share online. It’s “the new oil.”

And with the increase in value, the volume of data has been sky rocketing. Managing all of this data is becoming ever harder, with disparate data sources being just one of many headaches for data professionals. That’s why much of our blog focusses on best practices in managing Any² – any data, anywhere.

To join the experts, and stay up to date with the erwin blog click here.

 

erwin blog

Categories
erwin Expert Blog

SQL, NoSQL or NewSQL: Evaluating Your Database Options

A common question in the modern data management space involves database technology: SQL, NoSQL or NewSQL?

But there isn’t a one-size-fits-all answer. What’s “right” must be evaluated on a case-by-case basis and is dependent on data maturity.

For example, a large bookstore chain with a big-data initiative would be stifled by a SQL database. The advantages that could be gained from analyzing social media data (for popular books, consumer buying habits) couldn’t be realized effectively through sequential analysis. There’s too much data involved in this approach, with too many threads to follow.

However, an independent bookstore isn’t necessarily bound to a big-data approach because it may not have a mature data strategy. It might not have ventured beyond digitizing customer records, and a SQL database is sufficient for that work.

Having said that, the “SQL, NoSQL or NewSQL” question is gaining prominence because businesses are becoming increasingly data-driven.

In 2019, an IDC study found 85% of enterprise decision-makers said they had a time frame of two years to make significant inroads into digital transformation or they will fall behind their competitors and suffer financially. Furthermore, a Progress study showed that 85% of enterprise decision-makers feel they only have two years to make significant digital-transformation progress before suffering financially and/or falling behind competitors.

Considering these statistics, what better time than now to evaluate your database technology? The “SQL, NoSQL or NewSQL question,” is especially important if you intend to become more data-driven.

SQL, NoSQL or NewSQL: Advantages and Disadvantages

SQL

SQL databases are tried and tested, proven to work on disks using interfaces with which businesses are already familiar.

As the longest-standing type of database, plenty of SQL options are available. This competitive market means you’ll likely find what you’re looking for at affordable prices.

Additionally, businesses in the earlier stages of data maturity are more likely to have a SQL database at work already, meaning no new investments need to be made.

However in the modern digital business context, SQL databases weren’t made to support the the three Vs of data. The volume is too high, the variety of sources is too vast, and the velocity (speed at which the data must be processed) is too great to be analyzed in sequence.

Furthermore, the foundational, legacy IT world they were purpose-built to serve has evolved. Now, corporate IT departments must be agile, and their databases must be agile and scalable to match.

NoSQL

Despite its name, “NoSQL” doesn’t mean the complete absence of the SQL database approach. Rather, it works as more of a hybrid. The term is a contraction of “not only SQL.”

So, in addition to the advantage of continuity that staying with SQL offers, NoSQL enjoys many of the benefits of SQL databases.

The key difference is that NoSQL databases were developed with modern IT in mind. They are scalable, agile and purpose-built to deal with disparate, high-volume data.

Hence, data is typically more readily available and can be changed, stored or handle the insertion of new data more easily.

For example, MongoDB, one of the key players in the NoSQL world, uses JavaScript Object Notation (JSON). As the company explains, “A JSON database returns query results that can be easily parsed, with little or no transformation.” The open, human- and machine-readable standard facilitates data interchange and can store records, “just as tables and rows store records in a relational database.”

Generally, NoSQL databases are better equipped to deal with other non-relational data too. As well as JSON, NoSQL supports log messages, XML and unstructured documents. This support avoids the lethargic “schema-on-write,” opting to “schema-on-read” instead.

NewSQL

NewSQL refers to databases based on the relational (SQL) database and SQL query language. In an attempt to solve some of the problems of SQL, the likes of VoltDB and others take a best-of-both-worlds approach, marrying the familiarity of SQL with the scalability and agile enablement of NoSQL.

However, as with most seemingly win-win opportunities, NewSQL isn’t without its caveats. These vary from vendor to vendor, but in essence, you either have to sacrifice familiarity side or scalability.

If you’d like to speak with someone at erwin about SQL, NoSQL or NewSQL in more detail, click here.

For more industry advice, subscribe to the erwin Expert Blog.

Benefits of NoSQL Data Modeling

Categories
erwin Expert Blog

Data Modeling is Changing – Time to Make NoSQL Technology a Priority

Make NoSQL technology a priority

As the amount of data enterprises are tasked with managing increases, the benefits of NoSQL technology are becoming more apparent. 

Continue reading “Data Modeling is Changing – Time to Make NoSQL Technology a Priority”
Categories
erwin Expert Blog

NoSQL Database Adoption Is Poised to Explode

NoSQL database technology is gaining a lot of traction across industry. So what is it, and why is it increasing in use?

Techopedia defines NoSQL as “a class of database management systems (DBMS) that do not follow all of the rules of a relational DBMS and cannot use traditional SQL to query data.”

The rise of the NoSQL database

The rise of NoSQL can be attributed to the limitations of its predecessor. SQL databases were not conceived with today’s vast amount of data and storage requirements in mind.

Businesses, especially those with digital business models, are choosing to adopt NoSQL to help manage “the three Vs” of Big Data: increased volume, variety and velocity. Velocity in particular is driving NoSQL adoption because of the inevitable bottlenecks of SQL’s sequential data processing.

MongoDB, the fastest-growing supplier of NoSQL databases, notes this when comparing the traditional SQL relational database with the NoSQL database, saying “relational databases were not designed to cope with the scale and agility challenges that face modern applications, nor were they built to take advantage of the commodity storage and processing power available today.”

With all this in mind, we can see why the NoSQL database market is expected to reach $4.2 billion in value by 2020.

What’s next and why?

We can expect the adoption of NoSQL databases to continue growing, in large part because of Big Data’s continued growth.

And analysis indicates that data-driven decision-making improves productivity and profitability by 6%.

Businesses across industry appear to be picking up on this fact. An EY/Nimbus Ninety study found that 81% of companies understand the importance of data for improving efficiency and business performance.

However, understanding the importance of data to modern business isn’t enough. What 100% of organizations need to grasp is that strategic data analysis that produces useful insights has to start from a stable data management platform.

Gartner indicates that 90% of all data is unstructured, highlighting the need for dedicated data modeling efforts, and at a wider level, data management. Businesses can’t leave that 90% on the table because they don’t have the tools to properly manage it.

This is the crux of the Any2 data management approach – being able to manage “any data” from “anywhere.” NoSQL plays an important role in end-to-end data management by helping to accelerate the retrieval and analysis of Big Data.

The improved handling of data velocity is vital to becoming a successful digital business, one that can effectively respond in real time to customers, partners, suppliers and other parties, and profit from these efforts.

In fact, the velocity with which businesses are able to harness and query large volumes of unstructured, structured and semi-structured data in NoSQL databases makes them a critical asset for supporting modern cloud applications and their scale, speed and agile development demands.

For more data advice and best practices, follow us on Twitter, and LinkedIn to stay up to date with the blog.

For a deeper dive into Taking Control of NoSQL Databases, get the FREE eBook below.

Benefits of NoSQL

Categories
erwin Expert Blog

Data Modeling in a Jargon-filled World – NoSQL/NewSQL

erwin DM NoSQL Model

In the first two posts of this series, we focused on the “volume” and “velocity” of Big Data, respectively.  In this post, we’ll cover “variety,” the third of Big Data’s “three Vs.” In particular, I plan to discuss NoSQL and NewSQL databases and their implications for data modeling.

As the volume and velocity of data available to organizations continues to rapidly increase, developers have chafed under the performance shackles of traditional relational databases and SQL.

An astonishing array of database solutions have arisen during the past decade to provide developers with higher performance solutions for various aspects of managing their application data. These have been collectively labeled as NoSQL databases.

Originally NoSQL meant that “no SQL” was required to interface with the database. In many cases, developers viewed this as a positive characteristic.

However, SQL is very useful for some tasks, with many organizations having rich SQL skillsets. Consequently, as more organizations demanded SQL as an option to complement some of the new NoSQL databases, the term NoSQL evolved to mean “not only SQL.” This way, SQL capabilities can be leveraged alongside other non-traditional characteristics.

Among the most popular of these new NoSQL options are document databases like MongoDB. MongoDB offers the flexibility to vary fields from document to document and change structure over time. Document databases typically store data in JSON-like documents, making it easy to map to objects in application code.

As the scale of NoSQL deployments in some organizations has rapidly grown, it has become increasingly important to have access to enterprise-grade tools to support modeling and management of NoSQL databases and to incorporate such databases into the broader enterprise data modeling and governance fold.

While document databases, key-value databases, graph databases and other types of NoSQL databases have added valuable options for developers to address various challenges posed by the “three Vs,” they did so largely by compromising consistency in favor of availability and speed, instead offering “eventual consistency.” Consequently, most NoSQL stores lack true ACID transactions, though there are exceptions, such as Aerospike and MarkLogic.

But some organizations are unwilling or unable to forgo consistency and transactional requirements, giving rise to a new class of modern relational database management systems (RDBMS) that aim to guarantee ACIDity while also providing the same level of scalability and performance offered by NoSQL databases.

NewSQL databases are typically designed to operate using a shared nothing architecture. VoltDB is one prominent example of this emerging class of ACID-compliant NewSQL RDBMS. The logical design for NewSQL database schemas is similar to traditional RDBMS schema design, and thus, they are well supported by popular enterprise-grade data modeling tools such as erwin DM.

Whatever mixture of databases your organization chooses to deploy for your OLTP requirements on premise and in the cloud – RDBMS, NoSQL and/or NewSQL – it’s as important as ever for data-driven organizations to be able to model their data and incorporate it into an overall architecture.

When it comes to organizations’ analytics requirements, including data that may be sourced from a wide range of NoSQL, NewSQL RDBMS and unstructured sources, leading organizations are adopting a variety of approaches, including a hybrid approach that many refer to as Managed Data Lakes.

Please join us next time for the fourth installment in our series: Data Modeling in a Jargon-filled World – Managed Data Lakes.

nosql

Categories
erwin Expert Blog

Multi-tenancy vs. Single-tenancy: Have We Reached the Multi-Tenant Tipping Point?

Multi-tenancy vs. single-tenancy cloud hosting

The multi-tenancy vs. single-tenancy hosting debate has raged for years. Businesses’ differing demands have led to a stalemate, with certain industries more likely to lean one way than the other.

But with advancements in cloud computing and storage infrastructure, the stalemate could be at the beginning of its end.

To understand why multi-tenancy hosting is gaining traction over single-tenancy, it’s important to understand the fundamental differences.

Multi-Tenancy vs. Single-Tenancy

Gartner defines multi-tenancy as: “A reference to the mode of operation of software where multiple independent instances of one or multiple applications operate in a shared environment. The instances (tenants) are logically isolated, but physically integrated.”

The setup is comparable to that of a bank. The bank houses the assets of all customers in one place, but each customer’s assets are stored separately and securely from one another. Yet every bank customer still uses the same services, systems and processes to access the assets that belong to him/her.

The single-tenancy counterpart removes the shared infrastructure element described above. It operates on a one customer (tenant) per instance basis.

The trouble with the single-tenancy approach is that those servers are maintained separately by the host. And of course, this comes with costs – time as well as money – and customers have to foot the bill.

Additionally, the single-tenancy model involves tenants drawing from the power of a single infrastructure. Businesses with thorough Big Data strategies (of which numbers are increasing), need to be able to deal with a wide variety of data sources. The data is often high in volume, and must be processed at increasingly high velocities (more on the Three Vs of Big Data here).

Such businesses need greater ‘elasticity’ to operate efficiently, with ‘elasticity’ referring to the ability to scale resources up and down as required.

Along with cost savings and greater elasticity, multi-tenancy is also primed to make things easier for the tenant from the ground up. The host upgrades systems on the back-end, with updates instantly available to tenants. Maintenance is handled on the host side as well, and only one set of code is needed for delivering, greatly increasing the speed at which new updates can be made.

Given these considerations, it’s hard to fathom why the debate over multi-tenancy vs. single-tenancy has waged for so long.

Diminishing Multi-Tenancy Concerns

The advantages of cost savings, scalability and the ability to focus on improving the business, rather than up-keep, would seem to pique the interest of any business leader.

But the situation is more nuanced than that. Although all businesses would love to take advantage of multi-tenancy’s obvious advantages, shared infrastructure remains a point of contention for some.

Fears about host data breaches are valid and flanked by externally dictated downtime.

But these fears are now increasingly alleviated by sound reassurances. Multi-tenancy hosting initially spun out of single-tenancy hosting, and the fact it wasn’t built for purpose left gaps.

However, we’re now witnessing a generation of purpose-built, multi-tenancy approaches that address the aforementioned fears.

Server offloading means maintenance can happen without tenant downtime and widespread service disruption.

Internal policies and improvements in the way data is managed and siloed on a tenant-by-tenant basis serve to squash security concerns.

Of course, shared infrastructure will still be a point of contention in some industries, but we’re approaching a tipping point as evidenced by the success of such multi-tenancy hosts as Salesforce.

Through solid multi-tenancy strategy, Salesforce has dominated the CRM market, outstripping the growth of its contemporaries. Analysts expect further growth this year to match the uptick in cloud adoption.

What are your thoughts on multi-tenancy vs. single tenancy hosting?

Data-Driven Business Transformation

Categories
erwin Expert Blog

Data Modeling in a Jargon-filled World – Internet of Things (IoT)

Internet of Things

In the first post of this blog series, we focused on jargon related to the “volume” aspect of Big Data and its impact on data modeling and data-driven organizations. In this post, we’ll focus on “velocity,” the second of Big Data’s “three Vs.”

In particular, we’re going to explore the Internet of Things (IoT), the constellation of web-connected devices, vehicles, buildings and related sensors and software. It’s a great time for this discussion too, as IoT devices are proliferating at a dizzying pace in both number and variety.

Though IoT devices typically generate small “chunks” of data, they often do so at a rapid pace, hence the term “velocity.” Some of these devices generate data from multiple sensors for each time increment. For example, we recently worked with a utility that embedded sensors in each transformer in its electric network and then generated readings every 4 seconds for voltage, oil pressure and ambient temperature, among others.

While the transformer example is just one of many, we can quickly see two key issues that arise when IoT devices are generating data at high velocity. First, organizations need to be able to process this data at high speed.  Second, organizations need a strategy to manage and integrate this never-ending data stream. Even small chunks of data will accumulate into large volumes if they arrive fast enough, which is why it’s so important for businesses to have a strong data management platform.

It’s worth noting that the idea of managing readings from network-connected devices is not new. In industries like utilities, petroleum and manufacturing, organizations have used SCADA systems for years, both to receive data from instrumented devices to help control processes and to provide graphical representations and some limited reporting.

More recently, many utilities have introduced smart meters in their electricity, gas and/or water networks to make the collection of meter data easier and more efficient for a utility company, as well as to make the information more readily available to customers and other stakeholders.

For example, you may have seen an energy usage dashboard provided by your local electric utility, allowing customers to view graphs depicting their electricity consumption by month, day or hour, enabling each customer to make informed decisions about overall energy use.

Seems simple and useful, but have you stopped to think about the volume of data underlying this feature? Even if your utility only presents information on an hourly basis, if you consider that it’s helpful to see trends over time and you assume that a utility with 1.5 million customers decides to keep these individual hourly readings for 13 months for each customer, then we’re already talking about over 14 billion individual readings for this simple example (1.5 million customers x 13 months x over 30 days/month x 24 hours/day).

Now consider the earlier example I mentioned of each transformer in an electrical grid with sensors generating multiple readings every 4 seconds. You can get a sense of the cumulative volume impact of even very small chunks of data arriving at high speed.

With experts estimating the IoT will consist of almost 50 billion devices by 2020, businesses across every industry must prepare to deal with IoT data.

But I have good news because IoT data is generally very simple and easy to model. Each connected device typically sends one or more data streams with each having a value for the type of reading and the time at which it occurred. Historically, large volumes of simple sensor data like this were best stored in time-series databases like the very popular PI System from OSIsoft.

While this continues to be true for many applications, alternative architectures, such as storing the raw sensor readings in a data lake, are also being successfully implemented. Though organizations need to carefully consider the pros and cons of home-grown infrastructure versus time-tested industrial-grade solutions like the PI System.

Regardless of how raw IoT data is stored once captured, the real value of IoT for most organizations is only realized when IoT data is “contextualized,” meaning it is modeled in the context of the broader organization.

The value of modeled data eclipses that of “edge analytics” (where the value is inspected by a software program while inflight from the sensor, typically to see if it falls within an expected range, and either acted upon if required or allowed simply to pass through) or simple reporting like that in the energy usage dashboard example.

It is straightforward to represent a reading of a particular type from a particular sensor or device in a data model or process model. It starts to get interesting when we take it to the next step and incorporate entities into the data model to represent expected ranges –  both for readings under various conditions and representations of how the devices relate to one another.

If the utility in the transformer example has modeled that IoT data well, it might be able to prevent a developing problem with a transformer and also possibly identify alternate electricity paths to isolate the problem before it has an impact on network stability and customer service.

Hopefully this overview of IoT in the utility industry helps you see how your organization can incorporate high-velocity IoT data to become more data-driven and therefore more successful in achieving larger corporate objectives.

Subscribe and join us next time for Data Modeling in a Jargon-filled World – NoSQL/NewSQL.

Data-Driven Business Transformation

Categories
erwin Expert Blog

erwin Brings NoSQL into the Enterprise Data Modeling and Governance Fold

“NoSQL is not an option — it has become a necessity to support next-generation applications.”

Continue reading “erwin Brings NoSQL into the Enterprise Data Modeling and Governance Fold”
Categories
erwin Expert Blog

Data Modeling in a Jargon-filled World – Big Data & MPP

Big Data & MPP

By now, you’ve likely heard a lot about Big Data. You may have even heard about “the three Vs” of Big Data. Originally defined by Gartner, “Big Data is “high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision-making, insight discovery and process optimization.”

Continue reading “Data Modeling in a Jargon-filled World – Big Data & MPP”
Categories
erwin Expert Blog

Five Steps to Digital Transformation

Digital Transformation

Digital transformation is ramping up in all industries. Facing regular market disruptions, and landscape-changing technological breakthroughs, modern businesses must be both malleable and willing to change.

To stay competitive, you must be agile.

Digital Transformation is Inevitable

Increasing numbers of organizations are undergoing a digital transformation. The tried-and-tested yet rigid methods of doing business are being replaced by newer, data-orientated approaches that require thorough but fast analysis.

Some businesses – like Amazon, Netflix and Uber – are leading this evolution. They all provide very different services, but at their core, they are technology focused.

And they’re reaping rewards for it too. Amazon is one of the most valuable businesses in the world, perhaps one of the first companies to reach a $1-trillion valuation.

It’s not too late to adopt digital transformation, but it is  too late to keep fighting against it. The tide of change has quickened, and stubborn businesses could be washed away.

But what’s the best way to get started?

Step One: Determine Your End Goal

Any form of change must start with the end in mind, as it’s impossible to make a transformation without understanding why and how.

Before you make a change, big or small, you need to ask yourself why are we doing this? What are the positives and negatives? And if there are negatives, what can we do to mitigate them?

To ensure a successful digital transformation, it’s important to plot your journey from the beginning through your end goal, understanding how one change or a whole series of changes will alter your business.

Business process modeling tools can help map your digital transformation journey.

Step Two: Get Some Strategic Support

For businesses of any size, transformational change can disrupt day-to-day operations. In most organizations, the expertise to manage a sizeable transformation program doesn’t exist, and from the outset, it can appear quite daunting.

If your goal is to increase profits, it can seem contradictory to pay for support to drive your business forward. However, a slow or incorrect transformational process can be costly in many ways. Therefore, investing in support can be one of the best decisions you make.

Effective strategic planning, rooted in enterprise architecture, can help identify gaps and potential oversights in your strategy. It can indicate where investment is needed and ensure transformative endeavors aren’t undermined by false-starts and U-turns.

Many businesses would benefit further by employing strategic consultants. As experts in their fields, strategic consultants know the right questions to ask to uncover the information you need to influence change.

Their experience can support your efforts by identifying and cataloging underlying components, providing input to the project plan and building the right systems to capture important data needed to meet the business’s transformation goals.

Step Three: Understand What You Have

Once you know where you want to go, it’s important to understand what you currently do. That might seem clear, but even the smallest organizations are underpinned by thousands of business processes.

Before you decide to change something, you need to understand everything about what you currently do, or else a change could have an unanticipated and negative impact.

Enterprise architecture will also benefit a business here, uncovering strategic improvement opportunities – valuable changes you might not have seen.

As third-parties, consultants can provide an impartial view, rather than letting historic or legacy decisions cloud future judgment.

Businesses will also benefit from data modeling. This is due to the exponential increase in the volume of data businesses have to manage – as well as the variety of disparate sources.

Data modeling will ensure data is accessible, understood and better prepared for analysis and the decision-making process.

Step Four: Collect Knowledge from Within

Your employees are a wealth of knowledge and ideas, so it’s important to involve them in the enterprise architecture process.

Consultants can facilitate a series of staff workshops to enable employee insights to be shared and then developed into real, actionable changes.

Step Five: Get Buy-in Across the Business

Once you’ve engaged with your staff to collect the knowledge they hold, make sure you don’t cut them off there. Business change is only successful if everyone understands what is happening and why, with continuous updates.

Ensure that you take your employees through the change process, making them  part of the digital transformation journey.

Evidence suggests that 70 percent of all organizational change efforts fail, with a primary reason being that executives don’t get enough buy-in for new initiatives and ideas.

By involving relevant stakeholders in the strategic planning process, you can mitigate this risk. Strategic planning tools that enable collaboration can achieve this. Thanks to technological advancements in the cloud, collaboration can even be effectively facilitated online.

Take your employees through your digital transformation journey, and you’ll find them celebrating with you when you arrive at your goal.

If you think now is the right time for your business to change, get in touch with us today.

Data-Driven Business Transformation