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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Think A Data Lake Is THE Answer? Think Again. Here Comes Elastic Analytics

Brian  Hopkins

Enterprise architects, are you mired in a tangled web of data marts while your business pursues customer engagement without you? If you think a Hadoop-centric architecture is going to save the day, you may need to rethink. Your customers expect you to create systems of insight to deliver win-win engagement in real time. I'm seeing a new class of digital predators leverage the cloud to do just this. For example, Netflix designs cover graphics for its series based on subscriber viewing habits. They know their customers that well.

I call their technology approach an Elastic Analytics Platform in my recently published report. I formally define it as:

"A combination of data storage and middleware technology that allows the creation and dissolution of analytics components on demand, while provisioning these with data from one, or a few, distributed, virtualized data sources."

That's a mouthful. So here's a rough picture:

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Forrester's Annual ECM Panel Survey, 2015. Call for Participation — Deadline July 31, 2015

Cheryl McKinnon

Forrester's survey for ECM decision-makers is open, and we're looking for your participation! Take this opportunity to provide your perspectives on the key vendors, the challenges, and the opportunities you see in this technology market. This survey is intended for ECM decision-makers or influencers in end user organizations. This is not for ECM vendors or systems integrators . . . but vendors and consultants — we would love it if you could share this survey invitation with your customers. The survey will remain open until end of day Friday, July 31, 2015.

Why is your input important? Forrester uses this data to:

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The Rebirth Of iManage: A New Company With A Familiar Name Re-Enters The ECM Market

Cheryl McKinnon

Another week, another divestiture in the content management and collaboration market. A new - or more accurately, a re-newed - player enters the Enterprise Content Management market this week as iManage and HP make an apparently amicable split. Executives with longstanding roots in the iManage and Interwoven businesses, including Neil Araujo and Dan Carmel, have executed a management buyout to spin a revitalized iManage business out of HP’s Software division. iManage's press

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Let's Break All The Data Rules!

Michele Goetz

When I think about data, I can't help but think about hockey. As a passionate hockey mom, it's hard to separate my conversations about data all week with clients from the practices and games I sit through, screaming encouragement to my son and his team (sometimes to the embarrassment of my husband!). So when I recently saw a documentary on the building of the Russian hockey team that our miracle US hockey team beat at the 1980 Olympics, the story of Anatoli Tarsov stuck with me. 

Before the 1960s, Russia didn't have a hockey team. Then the Communist party determined that it was critical that Russia build one — and compete on the world stage. They selected Anatoli Tarsov to build the team and coach. He couldn't see films on hockey. He couldn't watch teams play. There was no reference on how to play the game. And yet, he built a world-class hockey club that not only beat the great Nordic teams but went on to crush the Canadian teams that were the standard for hockey excellence.

This is a lesson for us all when it comes to data. Do we stick with our standards and recipes from Inmon and Kimball? Do we follow check-box assessments from CMMI, DM-BOK, or TOGAF's information architecture framework? Do we rely on governance compliance to police our data?

Or do we break the rules and create our own that are based on outcomes and results? This might be the scarier path. This might be the riskier path. But do you want data to be where your business needs it, or do you want to predefine, constrain, and bias the insight?

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