Last week, I participated in a roundtable during a conference in Paris organized by the French branch of DAMA, the data management international organization. During the question/answer part of the conference, it became clear that most of the audience was confusing data management with data governance (DG). This is a challenge my Forrester colleague Michele Goetz identified early in the DG tooling space. Because data quality and master data management embed governance features, many view them as data governance tooling. But the reality is that they remain data management tooling — their goal is to improve data quality by executing rules. This tooling confusion is only a consequence of how much the word governance is misused and misunderstood, and that leads to struggling data governance efforts.
So what is “governance”? Governance is the collaboration, organization, and metrics facilitating a decision path between at least two conflicting objectives. Governance is finding the acceptable balance between the interests of two parties. For example, IT governance is needed when you would like to support all possible business projects but you have limited budget, skills, or resources available. Governance is needed when objectives are different for different stakeholders, and the outcome of governance is that they do not get the same priority. If everyone has the same objective, then this is data management.
You’ve heard it before but we said it again – this time in our recent webinar. There's a new kid in town: the chief data officer. Why the new role? Because of an increasing awareness of the value of data and the painful recognition of an inability to take advantage of the opportunities that it provides — due to technology, business, or basic cultural barriers. That was the topic of our webinar presented to a full house a few days ago; we discussed our recent report, Top Performers Appoint Chief Data Officers. Fortunately for those who weren’t there, the presentation – Chief Data Officers Cross The Chasm – is available (to clients) for download.
As the title suggests, chief data officers are no longer just for the early adopters – those enthusiasts and visionaries on the forefront of new technology trends. With 45% of global companies having appointed a chief data officer (not to be confused with a chief digital officer, as we specifically asked about “data”) and another 16% planning to make an appointment in the next 12 months – according to Forrester's Business Technographics surveys, the role of the chief data officer really has move into the mainstream.
However, there remain many companies who are not sure of whether they need a CDO or not. Many of those in our audience fell into that category. We asked two questions of the audience to gauge their interest and their actions to improve their data maturity:
Are you making organizational changes specifically to improve your data capabilities?
Gene Leganza and I just published a report on the role of the Chief Data Officer that we’re hearing so much about these days – Top Performers Appoint Chief Data Officers. To introduce the report, we sat down with our press team at Forrester to talk about the findings, and the implications for our clients.
Forrester PR: There's a ton of fantastic data in the report around the CDO. If you had to call out the most surprising finding, what would top your list?
Gene: No question it's the high correlation between high-performing companies and those with CDOs. Jennifer and I both feel that strong data capabilities are critical for organizations today and that the data agenda is quite complex and in need of strong leadership. That all means that it's quite logical to expect a correlation between strong data leadership and company performance - but given the relative newness of the CDO role it was surprising to see firm performance so closely linked to the role.
Of course, you can't infer cause and effect from correlation – the data could mean that execs in high-performing companies think having a CDO role is a good idea as much as it could mean CDOs are materially contributing to high performance. Either way that single statistic should make one take a serious look at the role in organizations without clear data leadership.
Outside of Tempe is a place called Sahuarita, Arizona. Sahuarita is the home of Air Force Silo #571-7 where a Titan missile, that was part of the US missile defense system and had a nine-megaton warhead that was at the ready for 25 years, should the United States need to retaliate against a Soviet nuclear attack. This missile could create a fireball two miles wide, contaminate everything within 900 square miles, hit its target in 35 minutes, and nothing in the current US nuclear arsenal comes close to its power. What kept it secure for 25 years? You guessed it...four phones, two doors, a scrap of paper, and a lighter.
Photo Credit: Renee Murphy
Technology has grown by leaps and bounds since the cold war. When these siloes went into service, a crew supplied by the Air Force manned them. These men and women were responsible for ensuring the security and availability of the missile. Because there was no voice recognition, retinal scanning, biometric readers, and hard or soft tokens, the controls that were in place were almost entirely physical controls. All of the technology that we think of as keeping our data and data centers secure hadn’t been developed yet. It is important to note that there was never a breach. Ever.
It might be an occupational hazard, but I can relate almost anything to security and risk management, and my visit to the Titan Missile Museum at AF Silo #571-7 was no exception. The lesson I took from my visit: there's room for manual controls in security and risk management.
There are multiple maturity models and associated assessments for data governance on the market. Some are from software vendors, or from consulting companies, which use these as the basis for selling services. Others are from professional groups like the one from the Data Governance Council.
They are all good – but frankly not adequate for the data economy many companies are entering into. I think it is useful to reshuffle some too well established ideas...
Maturity models in general are attractive because:
Using a maturity model is nearly a “no-brainer” exercise. You run an assessment and determine your current maturity level. Then you can make a list of the actions which will drive you to the next level. You do not need to ask your business for advice, nor involve too many people for interviews.
Most data governance maturity models are modeled on the very well known CMMI. That means that they are similar at least in terms of structure/levels. So the debate between the advantages of one vs another is limited to its level of detail.
As an analyst on Forrester's Customer Insight's team, I spend a lot of time counseling clients on best-practice customer data usage strategies. And if there's one thing I've learned, it's that there is no such thing as a 360-degree view of the customer.
Here's the cold, hard truth: you can't possibly expect to know your customer, no matter how much data you have, if all of that data 1) is about her transactions with YOU and you 2) is hoarded away from your partners. And this isn't just about customer data either -- it's about product data, operational data, and even cultural-environmental data. As our customers become more sophisticated and collaborative with each other ("perpetually connected"), so organizations must do the same. That means sharing data, creating collaborative insight, and becoming willing participants in open data marketplaces.
Now, why should you care? Isn't it kind of risky to share your hard-won data? And isn't the data you have enough to delight your customers today? Sure, it might be. But I'd put money on the fact that it won't be for long, because digital disruptors are out there shaking up the foundations of insight and analytics, customer experience, and process improvement in big ways. Let me give you a couple of examples:
Broadens the definition of metadata beyond “data on data” to include business rules, process models, application parameters, application rights, and policies.
Provides guidance to help evangelize to the business the importance of metadata, not by talking about metadata but by pointing out the value it provides against risks.
Recommends demonstrating to IT the transversality of metadata to IT internal siloed systems.
Advocates extending data governance to include metadata. The main impact of data governance should be to build the life cycle for metadata, but data governance evangelists reserve little concern for metadata at this point.
I will co-author the next document on metadata with Gene Leganza; this document will develop the next practice metadata architecture based partially but not only on a metadata exchange infrastructure. For a lot of people, metadata architecture is a Holy Grail. The upcoming document will demonstrate that metadata architecture will become an important step to ease the trend called “industrialization of IT,” sometimes also called “ERP for IT” or “Lean IT.”
In preparation for this upcoming document, please share with us your own experiences in bringing more attention to metadata.
The following question comes from many of our clients: what are some of the advantages and risks of implementing a vendor provided analytical logical data model at the start of any Business Intelligence, Data Warehousing or other Information Management initiatives? Some quick thoughts on pros and cons:
Leverage vendor knowledge from prior experience and other customers
May fill in the gaps in enterprise domain knowledge
Best if your IT dept does not have experienced data modelers
May sometimes serve as a project, initiative, solution accelerator
May sometimes break through a stalemate between stakeholders failing to agree on metrics, definitions
May sometimes require more customization effort, than building a model from scratch
May create difference of opinion arguments and potential road blocks from your own experienced data modelers
May reduce competitive advantage of business intelligence and analytics (since competitors may be using the same model)
Goes against “agile” BI principles that call for small, quick, tangible deliverables
Goes against top down performance management design and modeling best practices, where one does not start with a logical data model but rather
Defines departmental, line of business strategies
Links goals and objectives needed to fulfill these strategies
Defines metrics needed to measure the progress against goals and objectives
Defines strategic, tactical and operational decisions that need to be made based on metrics
Our latest BI maturity survey results are in. We used exactly the same questions from our online BI maturity self assessment tool to survey over 200 Forrester clients. Now you can compare your own BI maturity level against your peers by using data from the survey.
In the self assessment tool and in the survey we ask over 30 questions in the following 6 categories
Data and technology
Our clients rated themselves on the scale of 1 to 5 (5, if they strongly agree with our statement or 1, if they strongly disagree). Here are the overall results. Keep in mind that these results do not evaluate BI maturity accross ALL business, but rather in businesses that are already pretty far ahead in their BI implementations (they are Forrester clients, they read our research reports, they talk to our research analysts):