Data Quality Reboot Series For Big Data: Part 2 Persistence Vs. Disposable Quality

Michele Goetz

We last spoke about how to reboot our thinking on master data to provide a more flexible and useful structure when working with big data. In the structured data world, having a model to work from provides comfort. However, there is an element of comfort and control that has to be given up with big data, and that is our definition and the underlying premise for data quality.

Current thinking: Persistence of cleansed data.For years data quality efforts have focused on finding and correcting bad data. We used the word “cleansing” to represent the removal of what we didn’t want, exterminating it like it was an infestation of bugs or rats. Knowing what your data is, what it should look like, and how to transform it into submission defined the data quality handbook. Whole practices were stood up to track data quality issues, establish workflows and teams to clean the data, and then reports were produced to show what was done. Accomplishment was the progress and maintenance of the number of duplicates, complete records, last update, conformance to standards, etc. Our reports may also be tied to our personal goals. Now comes big data — how do we cleanse and tame that beast?

Reboot: Disposability of data quality transformation. The answer to the above question is, maybe you don’t. The nature of big data doesn’t allow itself to traditional data quality practices. The volume may be too large for processing. The volatility and velocity of data change too frequently to manage. The variety of data, both in scale and visibility, is ambiguous.

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Business Needs To Take A Moneyball View On Performance

Craig Le Clair

I recently finished reading Moneyball, the Michael Lewis bestseller and slightly above-average Hollywood movie. It struck me how great baseball minds could be so off in their focus on the right metrics to win baseball games. And by now you know the story — paying too much for high batting averages with insufficient focus where it counts —metrics that correlate with scoring runs, like on-base percentage. Not nearly as dramatic — but business is having its own “Moneyball” experience with way too much focus on traditional metrics like productivity and quality and not enough on customer experience and, most importantly, agility.

Agility is the ability to execute change without sacrificing customer experience, quality, and productivity and is “the” struggle for mature enterprises and what makes them most vulnerable to digital disruption. Enterprises routinely cite the incredible length of time to get almost any change made. I’ve worked at large companies and it’s just assumed that things move slowly, bureaucratically, and inefficiently. But why do so many just accept this? For one thing, poor agility undermines the value of other collected BPM metrics. Strong customer experience metrics are useless if you can’t respond to them in a timely manner, and so is enhanced productivity if it only results in producing out-of-date products or services faster.

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Why Your Company Needs You To Attend Forrester’s Business Architecture & Process Summit

Alex Cullen

The pace of business change is accelerating. The reason why it is accelerating is the mushrooming of disruptive factors: your customers expecting anytime/everywhere access to you through their mobile devices, competitors leveraging big data technology to rapidly execute on customer-centric value propositions, and new market entrants with lean business models that enable them to outmaneuver your business.

Most companies deal poorly with disruptive change. If they are the “disruptor,” seeking to use these disruptive factors to steal market share, they often run without a plan and only after, for example, a poor mobile app customer experience, realize what they should have changed. If they are the firm being disrupted, the desire for a fast response leads to knee-jerk reactions and a thin veneer of new technology on a fossilized back-office business model.

This is where the value of business architects and business process professionals comes to play: you help your company plan and execute coherent responses to disruptive factors. That’s why your company needs you to attend Forrester’s Business Architecture & Process Forum: Embracing Digital Disruption in London on October 4 and Orlando, FL on October 18–19, 2012.

  • We’ll start with James McQuivey describing how technology is changing the playing field for disruption in his keynote: The Disruptor’s Handbook: How To Make The Most Of Digital Disruption.
  • We’ll look at how firms have used technology to rethink their operating models, eliminating low-value activities to focus on what their customers value in Craig Le Clair’s Implementing The Different In The Age Of Digital Disruption.
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It Doesn't Matter Where EA Lives — So Let's Stop Arguing About It

Brian  Hopkins

George Colony, our CEO, just released a post on his blog about enterprise architecture, aptly enough named “Enterprise Architects For Dummies (CEOs).” I retweeted the post to my followers and received a flood of responses, most of which were violently disagreeing with George’s assertion that EA works for the CIO. I think this is a pointless argument, but underscores a very important change that most are missing.

Here’s what I mean:

  • The objection to putting EA under the CIO is based on an old-school notion.That notion is that CIOs are chief technology infrastructure managers. Our data shows that the role of CIO is changing, fueled by cloud and other as-a-service technology. CTOs or VPs of IT are increasingly taking on the job we used to think of as the CIO, while progressive CIOs are evolving to something else. Locating EA under the CTO is a bad idea, we all agree.
  • Every business is a digital business.If you don’t believe me, I’ll send you a pile of research. There is no such thing as a non-information-centric business anymore — or at least there won’t be for very long, because they are going out of business. Forrester has been using the term “business technology” (BT) for a while to indicate that there is no room for having separate business and IT — it simply won’t work much longer. Even in the most paper, analog verticals, we can give you example after example; check out Monsanto’s IFS (they are a seed company!).
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Data Quality Reboot Series For Big Data: Part 1 Master Data

Michele Goetz

What data do you trust? Increasingly, business stakeholders and data scientists trust the information hidden in the bowels of big data. Yet, how data is mined mostly circumvents existing data governance and data architecture due to speed of insight required and support data discovery over repeatable reporting.

The key to this challenge is a data quality reboot: rethink what matters, and rethink data governance.

Part 1 of our Data Quality Reboot Series is to rethink master data management (MDM) in a big data world.

Current thinking: Master data as a single data entity. A common theme I hear from clients is that master data is about the linked data elements for a single record. No duplication or variation exists to drive consistency and uniqueness. Master data in the current thinking represents a defined, named entity (customer, supplier, product, etc.). This is a very static view of master data and does not account for the various dimensions required for what is important within a particular use case. We typically see this approach tied tightly to an application (customer resource management, enterprise resource management) for a particular business unit (marketing, finance, product management, etc.). It may have been the entry point for MDM initiatives, and it allowed for smaller scope tangible wins. But, it is difficult to expand that master data to other processes, analysis, and distribution points. Master data as a static entity only takes you so far, regardless of whether big data is incorporated into the discussion or not.

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Is Duct Tape Holding Together Your Data Architecture?

Michele Goetz

Let’s face it: managing data is not an easy task. The business certainly wishes, and may even think, that this is the case. So, we cut corners on fulfilling data requirements to meet short-term demands. We lay aside more strategic investment that would best support our strategies, have a wider value across the business, and build toward a proper foundation for the long term.

Today, our data architecture gets held together with duct tape. Even if we have used the new “pretty” duct tape that comes in colors, camouflage, and animal patterns, it is still duct tape.

What we are now faced with is more data silos, inconsistency in data quality, and challenges to provide a single view of your business. Investments made to provide a strong data foundation have either withered behind business as usual or have been collecting cobwebs from lack of use. I call this data technical debt, and it is holding your business back both in getting information the business needs and allowing for agility to meet the increasing variety of use cases.

To move forward, what are things we can do?

1.      Make sure there is a strong vision for a desired state.

2.     Recognize milestones needed to achieve the desired state.

3.     Continuously align project requests to milestones to ensure progress is made on the vision.

4.     Align and consolidate projects with similar milestone contributions to expand the value of vision widely and faster.

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The First Rule Of Big Data — Don't Talk About Big Data

Brian  Hopkins

I’ll be chairing Big Data World Europe on September 19 in London; in advance of that event, here are a few thoughts.

Since late 2011, we’ve seen the big data noise level eclipse cloud and even BYOD, and we are seeing the backlash too (see Death By Big Data, to which I tweeted, “Yes, I suppose, ‘too much of anything is a bad thing’”). The number one thing clients want to know is, “What is my competition doing? Give me examples I can talk to my business about.” These questions reflect a curiosity on the part of IT and a “peeking under the hood to see what’s there” attitude.

My advice is to start the big data journey with your feet on the ground and your head around what it really is. Here are some “rules” I’ve been using with folks I talk to:

First rule of big data: don’t talk about big data. The old adage holds true here — those that can do big data do it, those that can’t talk <yup, I see the irony :-)>. I was on the phone with a VP of analytics who reflected that her IT people were constantly bringing new technologies to them like a dog with a bone. Her general reaction is, show me the bottom-line value. So what to do? Instead of talking to your business about big data, find ways to solve problems more affordably with data at greater scale. Now that’s “doing big data.”

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Categories:

Forrester Wave For Master Data Management — Enterprise, Big Data, Data Governance

Michele Goetz

As the new analyst on the block at Forrester, the first question everyone is asking is, “What research do you have planned?” Just to show that I’m up for the task, rather than keeping it simple with a thoughtful report on data quality best practices or a maturity assessment on data management, I thought I’d go for broke and dive into the master data management (MDM) landscape. Some might call me crazy, but this is more than just the adrenaline rush that comes from doing such a project. In over 20 inquiries with clients in the past month, questions show increased sophistication in how managing master data can strategically contribute to the business.

What do I mean by this?

Number 1: Clients want to know how to bring together transitional data (structured) and content (semi-structured and unstructured) to understand the customer experience, improve customer engagement, and maximize the value of the customer. Understanding customer touch points across social media, e-commerce, customer service, and content consumption provides a single customer view that lets you customize your interactions and be highly relevant to your customer. MDM is at the heart of bringing this view together.

Number 2: Clients have begun to analyze big data within side projects as a way to identify opportunities for the business. This intelligence has reached the point that clients are now exploring how to distribute and operationalize these insights throughout the organization. MDM is the point that will align discoveries within the governance of master data for context and use.

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From Our EA Community — Boiling Down Your BT Strategy To A Single Page

Brian  Hopkins

Last fall, a member of our enterprise architecture community asked a simple question — how do you represent IT strategy on a single page? What resulted was the most read and commented discussion to date. That got our attention! But what really piqued our interest was when another community participant challenged us to go beyond our usual publishing process to co-create a report with the community.

For those who have been following the discussion, it has been slow going, but I'm glad to say that we are done! What's more, we have decided to make this report available to everyone since much of the content came directly from the community. Please follow this link (www.forrester.com/btstrategyonapage) to request your copy if you are not a client (free site registration is required). Clients should go to our normal site to download the report.

In the research, we took the community contributions and created a toolkit in PowerPoint form containing seven examples of business technology (BT) strategy representation on a single "page." The lesson we learned is that there is no one right way to do it and you will probably need several one-pagers for different audiences.

Why title it BT and not IT? We started out with the notion of pure IT strategy, but quickly realized that the best one-pagers married business strategy with technology strategy. Ideally, these two should be co-created by business and technology leaders. Why? Because "aligned IT" can no longer keep up with the blinding pace of business change; it takes a business technology approach. Consider:

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Build A High-Performance EA Practice

Gene Leganza

As the pace of change continues to accelerate in an increasingly complex business environment, organizations need to thoroughly understand how their business operates and plan the technology-fueled business transformation they'll need in the future. Establishing this understanding and enabling the transition to the future state have always been the concerns of enterprise architecture programs, and EA has emerged as a critical practice for managing an enterprise's evolution.

But EA programs have existed for more than a decade, and most of them have fallen short of these lofty goals. Why? Old-school EA has been too tactical, too technology-centric, or too disengaged from business priorities to have significant impact. Enterprises need a high-performance approach to EA that is laser-focused on driving business outcomes. To plan their future, organizations have the following alternatives:

  • Try to get there without a formal EA program.Enterprises that have yet to initiate an EA program — or have abandoned their effort — are operating without a coherent plan to evolve toward a clearly articulated future state. The lack of an EA program may not derail business as usual, but business change is likely to occur in a siloed, uncoordinated fashion.
  • Stick with the status quo EA program.Highly skilled and knowledgeable architects typically staff EA programs. But resources are typically focused on project-level activities. Strategy work is likely to be about technology road maps — not business capabilities. Isolating technology planning from business planning maintains the old-school, arms-length relationship between IT and the business.
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