Forrester’s 2016 Predictions: Turn Data Into Insight And Action

Brian  Hopkins

Three of four architects strive to make their firms data driven. But well-meaning technology managers only deal with part of the problem: How to use technology to glean deeper, faster insight from more data -- and more cheaply. But consider that only 29% of architects say their firms are good at connecting analytics results to business outcome. This is a huge gap! And the problem is the ‘data driven’ mentality that never fights it’s way out of technology and to what firms care about - outcomes.

In 2016, customer-obsessed leaders will leapfrog their competition, and we will see a shift as firms seek to grow revenue and transform customer experiences. Insight will become a key competitive weapon, as firms move beyond big data and solve problems with data driven thinking.

Shift #1 - Data and analytics energy will continue drive incremental improvement

In 2016, the energy around data-driven investments will continue to elevate the importance of data and create incremental improvement in business performance. In 2016, Forrester predicts:

  • Chief data officers will gain power, prestige and presence...for now.  But the long term viability of the role is unclear. Certain types of businesses, like digital natives, won’t benefit from appointing a CDO.
  • Machine learning will reduce the insight killer - time. Machine learning will  replace manual data wrangling and data governance dirty work. The freeing up of time will accelerate data strategies.
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IBM And Teradata — A Tale Of Two Vendors’ Struggle With Disruption

Brian  Hopkins

I said that 2015 would be a tough year for enterprise data and analytics vendors in my spring report, "Brief: Turning Big Data Into Business Insights, 2015." I thought two things would happen. First, open source would drag on vendors’ revenues as demand for big expensive products declined. Second, the cloud would create revenue headaches. Turns out, I was right. Teradata's midyear earnings were down 8%, and IBM reported that Q2 revenue was down 12% from a year ago. As further proof, consider the rash of data management vendors running for private equity (e.g. Dell/EMC, Informatica, and TIBCO). It’s been tough times indeed, even though most vendors are keeping their messaging positive to reassure buyers and investors.

Over the past two weeks, I attended Teradata Partners in Anaheim and IBM Insight in Las Vegas — giving me a firsthand look at how two giants of the data and analytics industry are handling disruption. What I saw was a tale of two vendors that couldn’t be any more different:

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Semantic Technology Is Not Only For Data Geeks

Michele Goetz

You can't bring up semantics without someone inserting an apology for the geekiness of the discussion. If you're a data person like me, geek away! But for everyone else, it's a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.

It begins with a seemingly innocent set of questions:

"Is there a better way to master my data?"

"Is there a better way to understand the data I have?"

"Is there a better way to bring data and content together?"

"Is there a better way to personalize data and insight to be relevant?"

Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They're born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester's Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized. 

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Agile Development And Data Management Do Coexist

Michele Goetz

A frequent question I get from data management and governance teams is how to stay ahead of or on top of the Agile development process that app dev pros swear by. New capabilities are spinning out faster and faster, with little adherence to ensuring compliance with data standards and policies. 

Well, if you can't beat them, join them . . . and that's what your data management pros are doing, jumping into Agile development for data. 

Forrester's survey of 118 organizations shows that just a little over half of organizations have implemented Agile development in some manner, shape, or form to deliver on data capabilities. While they lag about one to two years behind app dev's adoption, the results are already beginning to show in terms of getting a better handle on their design and architectural decisions, improved data management collaboration, and better alignment of developer skills to tasks at hand. 

But we have a long way to go. The first reason to adopt Agile development is to speed up the release of data capabilities. And the problem is, Agile development is adopted to speed up the release of data capabilities. In the interest of speed, the key value of Agile development is quality. So, while data management is getting it done, they may be sacrificing the value new capabilities are bringing to the business.

Let's take an example. Where Agile makes sense to start is where teams can quickly spin up data models and integration points in support of analytics. Unfortunately, this capability delivery may be restricted to a small group of analysts that need access to data. Score "1" for moving a request off the list, score "0" for scaling insights widely to where action will be taking quickly.

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Managing The Complexity Of Hybrid Cloud: Learn From Leading Chinese Firms

Charlie Dai

Cloud is becoming the new norm for enterprises. More and more companies across the globe are using a combination of two or more private, hosted, or public cloud services – applying different technology stacks to different business scenarios. Hybrid cloud management is now an important priority that enterprise architecture (EA) professionals should consider to support their organizations on the journey toward becoming a digital business.

I’ve recently published two reports focusing on how to manage the complexity of hybrid cloud. These reports analyze the key dimensions to consider for hybrid cloud management and present four steps to help move your firm further along the path to hybrid maturity. To unleash the power of digital business, analyze the strategic hybrid cloud management practices of visionary Chinese firms on their digital transformation journey. Some of the key takeaways:

  • Align hybrid cloud management capabilities with your level of maturity . Hybrid cloud maturity is a journey of digital transformationcovering four steps: initial acceptance, strategic adoption, hybrid operationalization, and hybrid autonomy; maturity is measured by familiarity with, experience with, and knowledge of how to operate cloud. EA pros should build their management capabilities step by step, aiming to unify and automate cloud managed services by understanding technical dependencies and business priorities.
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Are Data Preparation Tools Changing Data Governance?

Michele Goetz

First there was Hadoop. Then there were data scientists. Then came Agile BI on big data. Drum roll, please . . . bum, bum, bum, bum . . .

Now we have data preparation!

If you are as passionate about data quality and governance and I am, then the 5+-year wait for a scalable capability to take on data trust is amazingly validating. The era for "good enough" when it comes to big data is giving way to an understanding that the way analysts have gotten away with "good enough" was through a significant amount of manual data wrangling. As an analyst, it must have felt like your parents saying you can't see your friends and play outside until you cleaned your room (and if it's anything like my kids' rooms, that's a tall order).

There is no denying that analysts are the first to benefit from data preparation tools such as Altyrex, Paxata, and Trifacta. It's a matter of time to value for insight. What is still unrecognized in the broader data management and governance strategy is that these early forays are laying the foundation for data citizenry and the cultural shift toward a truly data-driven organization.

Today's data reality is that consumers of data are like any other consumers; they want to shop for what they need. This data consumer journey begins by looking in their own spreadsheets, databases, and warehouses. When they can't find what they want there, data consumers turn to external sources such as partners, third parties, and the Web. Their tool to define the value of data, and ultimately if they will procure it and possibly pay for it, is what data preparation tools help with. The other outcome of this data-shopping experience is that they are taking on the risk and accountability for the value of the data as it is introduced into analysis, decision-making, and automation.

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Enterprise Architecture Awards 2015 – Effectiveness And Innovation Lays The Path To Value

Alex Cullen

One of the winners of this year’s Forrester/Infoworld Enterprise Architecture Awards segmented their EA practice into two disciplines: Innovation Architecture and Effectiveness Architecture. These two words describe the range of winners selected by our judges.

Before I announce the winners, let me tell you about why these two words are significant. The Forrester/Infoworld EA Awards have always sought to uncover programs that impact their business through the insight and value that only EA can provide. But many EA programs struggle with this – and the reason for this struggle lies more in themselves than in their context. Bottom line: They focus on "doing architecture" or on "being smart technical experts." Many talk about being more business-focused but aren’t willing to change their thinking or how they engage with their business.

The five winners of this year’s awards have changed their approach to EA and delivering business impact, and the results show.

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Data Governance and Data Management Are Not Interchangeable

Michele Goetz

Since when did data management and data governance become interchangeable?

This is a question that has both confounded and frustrated me.  The pursuit of data management vendors to connect with business stakeholders, because of the increasing role business units have had in decison making and holding the purse strings to technology purchases, means data governance as a term was hijacked to snuff out the bad taste of IT data projects gone sour. 

The funny thing is, vendors actually began drinking their own marketing Kool-aid and think of their MDM, quality, security, and lifecycle management products as data governance tools/solutions.  Storage and virtualizations vendors are even starting to grock on to this claiming they govern data. Big data vendors jumped over data management altogether and just call their catalogs, security, and lineage capabilities data governance.  

Yes, this is a pet peeve of mine - just as data integration is now called blending, and data cleansing and transformation is now called wrangling or data preparation. But more on that is another blog...

First, you (vendor or data professional) cannot simply sweep the history of legacy data investments that were limited in results and painful to implement under the MadMen carpet. Own it and address the challenges through technology innovation rather than words.

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The Top Technology Trends To Watch: 2016 To 2018

Brian  Hopkins

Enterprise architects face more exciting — and greater — challenges as the age of the customer takes off. But technology invention, innovation, and spending are notoriously cyclical. In fact, our first tech trends report in 2009 predicted a boom cycle through 2016. And we have seen this — with social, mobile, cloud, analytics, and big data, to name just the obvious ones. A big finding of our research however is this: The Age Of The Customer has changed the classic technology investment cycle.  For example, technology management’s spend will grow about 5% in North America in 2016. This is a decent pace. However, spend on business technology — the things that let firms win, serve, and retain customers — will be double that!

All this new money will shift the focus of investment from point solution inventions toward “end-to-end innovation” by 2018. And by end-to-end, we mean across the customer life-cycle and customer journeys as opposed to classic 'enterprise integration'. This shift will happen in three phases:

  • Visionaries will dominate dawning phase trends as they drive point inventions to address specific business organizations’ opportunities.
  • Fast followers will discover the limits of point solutions in the awareness phase and begin to work through them.
  • Enterprises will shift investment toward integrating capabilities across the customer life cycle in the acceptance phase.
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You May Not Need A CDO — But Wouldn’t You Want To Improve Your Odds Of Success?

Gene Leganza

Jennifer Belissent 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 about the implications for our clients.

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