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Nowadays everyone agrees on the importance of the data governance. The understanding that it is critical to deliver trust, regulatory compliance and that it is a key element to deliver improved business has been well-accepted.

Successfully implementing data governance requires changes and investments in several domains:

  • People will need get trained and get support with their change. Don’t forget that certain co-workers will take up a new role (i.e. data steward), some people will need to share data ownership, etc. The necessary attention to change management is required.
  • Your business process will for sure benefit from optimizations but also require change. Important to realize in the data governance – process context is that you will become capable to tilt your organization from system or application silos to an approach where data is governed from a process point of view across your organizational landscape.
  • Also on a technology level you will need to optimize what you already have in place and most likely acquire data governance specific functionality that you currently lack. Think about a data governance capable business glossary, data catalog, etc.



Making it tangible

Understanding investments is one facet, getting value out of data governance is something else. How do you make all this tangible?

The most successful of our data governance clients focus on the next important areas:

Revenue Impact. 

Focus on identifying and addressing new business opportunities through data analytics & data science use. The only hard requirement is obviously that your data governance foundations should be in place. You might have the fanciest and most powerful analytical tooling available but without data governance, it remains like finding the magnetic north point with a faulty compass. Calculating the return of this comes down to considering the cost of the data related efforts and the potential business outcome. This is an exercise that will require input from all the involved stakeholders – both business and it. 


Business User Productivity.

Proper Data Governance is primarily an enabler. The business user area is a great example to illustrate this. Allowing them to move from finding data to applying data, directly results in increased their productivity and value to the organization. They are enabled to focus their core business instead of wasting huge amounts of time before they can start. A recent IDC study calculates the productivity gains will have an average value of €1572 per impacted user per year.

Operational Productivity.

Having better data quality, improved data controls, a connected data speech community, … will also generate operational benefits. No more waste of time due to ping-pong games caused by unclear roles and responsibilities, rework and churn due to dirty or incomplete data, …

Risk Mitigation. 

Data governance is key for compliance and audit purposes. Having visibility on data lineage, ownership and track and trace of data consumption is elementary for GRC teams. Having a proper data governance platform facilitates this and allows your teams to act more quickly and efficient. Governed automation vs ad-hoc manual effort is what this is all about. In this area IDC projects, in the same study, that organization can realize a benefit of €1280 per impacted user per year.

Besides operational efficiency, the direct cost elements of the overall risk can be calculated quiet easily. Think about the GDPR legislation where penalties are set up to €10 million, or 2% of the worldwide annual revenue of the prior financial year, whichever is higher.

Calculating the indirect costs elements is a bit more complicated. Think about the same GDPR example. The penalty issued for an infringement is clearly specified but imagine that your organization is active in a market vertical where reputation and being a trustworthy party is extremely important. In that type of scenario, getting a GDPR penalty will also have a big impact on your revenue and generate substantial costs to brush up your reputation. Calculating these costs requires organizational and market vertical specific insight.

 

With this info, you’re off to a good start. If you would require some practical advice and expertise, reach out to us.




Interested in the Data Governance?

Would you like to know how Datalumen can also help you with tour Data Governance initiative?  Contact us and start our data conversation.

Summer is here and the longer days it brings means more time available to spend with a ripping read. That’s how it ideally works at least. We selected 3 valuable books worth your extra time.

 

The Chief Data Officer’s Playbook

The issues and profession of the Chief Data Officer (CDO) are of significant interest and relevance to organisations and data professionals internationally. Written by two practicing CDOs, this new book offers a practical, direct and engaging discussion of the role, its place and importance within organisations. Chief Data Officer is a new and rapidly expanding role and many organisations are finding that it is an uncomfortable fit into the existing C-suite. Bringing together views, opinions and practitioners experience for the first time, The Chief Data Officer’s Playbook offers a compelling guide to anyone looking to understand the current (and possible future) CDO landscape.

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Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility

Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility, the first book ever written on the topic of data virtualization, introduces the technology that enables data virtualization and presents ten real-world case studies that demonstrate the significant value and tangible business agility benefits that can be achieved through the implementation of data virtualization solutions. The book introduces the relationship between data virtualization and business agility but also gives you  a more thorough exploration of data virtualization technology. Topics include what is data virtualization, why use it, how it works and how enterprises typically adopt it. 

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Start With Why

Simon Sinek started a movement to help people become more inspired at work, and in turn inspire their colleagues and customers. Since then, millions have been touched by the power of his ideas, including more than 28 million who’ve watched his TED Talk based on ‘Start With Why’ — the third most popular TED video of all time. Sinek starts with a fundamental question: Why are some people and organizations more innovative, more influential, and more profitable than others? Why do some command greater loyalty from customers and employees alike? Even among the successful, why are so few able to repeat their success over and over? 
 
People like Martin Luther King, Steve Jobs, and the Wright Brothers had little in common, but they all started with Why. They realized that people won’t truly buy into a product, service, movement, or idea until they understand the Why behind it.  ‘Start With Why’ shows that the leaders who’ve had the greatest influence in the world all think, act, and communicate the same way — and it’s the opposite of what everyone else does. Sinek calls this powerful idea The Golden Circle, and it provides a framework upon which organizations can be built, movements can be led, and people can be inspired. And it all starts with Why.

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Summer Giveaways

We’re giving away 50 copies of ‘Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility’.  Want to win? Just complete the form and cross your fingers. Good luck!


Winners are picked randomly at the end of the giveaway. Our privacy policy is available here.

Data Virtualization Top Use Cases
Data Virtualization is definitely on the rise. At its Data and Analytics Summit in London in March 2018, Gartner was projecting accelerated data virtualization adoption for both first-time and expanded deployments. Besides market analysts, we also see high demand and can confirm this being one of the hottest data solutions. But what are the top uses cases for data virtualization?


 


Interested in Data Virtualization?

Would you like to know how Datalumen can also help you understand how your organization can benefit from using Data Virtualization?  Contact us and start our data conversation.

Data Market Update
Forrester Research recently published The Forrester Wave™: Enterprise Data Virtualization, Q4 2017 report. The firm profiled 13 vendors in this report. The last Wave on this topic was published a while ago,  March 2015, with 9 vendors. Here is an overview of what has changed in the last two-and-a-half years.

Data Virtualization Market has Expanded

According to this Forrester report, the enterprise data virtualization market has expanded along multiple dimensions – customer adoption, more industries, more use cases, new players, and acquisitions.
 

  • More customer adoption – Forrester states customer adoption of data virtualization has been gaining momentum. In 2017, Forrester profiled 2,106 global technology decision makers for its Data Global Business Technographics Data and Analytics Survey, and found that “…56% of global technology decision makers in our 2017 survey tell us they have already implemented, are implementing, or are expanding or upgrading their implementations of DV technology, up from 45% in 2016.”
  • More industries – Forrester states that in its early years, data virtualization was primarily used in financial services, telecom, and government sectors. In the last 5 years, however, Forrester has found significant adoption of DV in insurance, retail, healthcare, manufacturing, oil and gas, and eCommerce verticals as well.
  • More use cases – Further, Forrester found that among the customers who have been using data virtualization, the deployment has increased from single-use case, primarily customer analytics, to a broader enterprise-wide use involving multiple use cases such as internet of things, fraud detection, and integrated insights.
  • New players – In the 2017 Enterprise Data Virtualization Wave report, four new vendors have been included implying, in our opinion, expanding data virtualization market.
  • Acquisitions – In signs that the data virtualization market is maturing, TIBCO Software recently acquired Cisco Information Server, thus entering the data virtualization market.

We think all these data points are significant indicators that the data virtualization market is a healthy, growing market that is reaching maturity.

Data Virtualization Poised for Further Growth Pushed Forward by Leaders

Forrester expects the data virtualization market to grow further “because more enterprise architecture (EA) professionals see data virtualization as critical to their enterprise data strategy.” It says that these EA Pros are looking to support more complex data virtualization deployments. To satisfy such needs, the leaders featured in the report provide high-end scale, security, modeling, and broad use case support with their mature product offerings. “The leaders we identified offer large and complex deployments, and they support a broader set of use cases and more mature data management capabilities,” Forrester says. It is worth noting that four of the past five leaders retained their Leaders positions, while one vendor slipped into the Strong Performers.

Read the Complete Report

The Forrester Wave: Enterprise Data Virtualization, Q4 2017 is a must read for enterprise architecture (EA) professionals. According to Forrester, “Enterprise data virtualization has become critical to every organization in overcoming growing data challenges. These platforms deliver faster access to connected data and support self-service and agile data-access capabilities for EA pros to drive new business initiatives.”




More info on Data Virtualization?

Would you like to know what
Data Virtualization can also mean for your organization?
Have a look at our Data Virtualization section 
and contact us.







Business intelligence & analytics today have dramatically shifted from the traditional IT-driven model to a modern self-service approach. This is due to a number of changes, including the fact that the balance of power has steadily shifted from IT to the business, and also the fact that the business community has new access to more innovative technologies that give them powerful analytical and visualization capabilities (e.g. Tableau, …).  This increased use and capability has put the business in the driver seat of much front-end BI decision-making. 

In order to help your business community continue to increase its self-service capabilities, there is one important, but often-overlooked item: Many implementations fail to realize their full potential because they fall into the trap of building out just the proverbial shop window, and forgetting the actual shop!  It is just as important to add increased accessibility and flexibility to the underlying data-layer (and ease the access, discovery, and governance of your data), as it is to provide users the front-end thru powerful analytics and visualization capabilities. 

With respect to self‐service analytics, four phases can be identified in the market. These also typically mirror how analytics are implemented in many of companies. The following diagram describes in four phases how data virtualization can strengthen and enrich the self‐service data integration capabilities of tools for reporting and analytics:

 

THE NEED FOR DATA PREPARATION AND DATA VIRTUALIZATION

To support both IT-driven and Business-driven BI, two techniques are required: data preparation, and data virtualization.   There are a many scenarios where you can use these techniques to strengthen and speedup the implementation of self‐service analytics:

  • Using data virtualization to operationalize user‐defined data sets
  • Using data virtualization as a data source for data preparation
  • Using data virtualization to make data sets developed with data preparation available for all users 

To learn about how to succeed in your data journey, feel free to contact us. More info about our full spectrum of data solutions is also available on the Datalumen website.

Read more in detail about the different scenario’s in the ‘Strengthening Self-Service Analytics with Data Preparation and Data Virtualization’ whitepaper. In addition, this whitepaper describes how these two BI forms can operate side by side in a cooperative fashion without lowering the level of self‐service for business users. In other words, it describes how the best of both worlds can be combined. This whitepaper is written by Rick Van Der Lans, an indepedent analyst and expert.