Without Data Management Standards – Anarchy!

Michele Goetz

 

When I posted a blog on Don’t Establish Data Management Standards (it was also on Information Management's website as Data Management Standards are a Barrier) I expected some resistance.  I mean, why post a blog and not have the courage to be provocative, right?  However, I have to say I was surprised at the level of resistance.  Although, I also have to point out that this blog was also one of the most syndicated and recommended I have had.  I will assume that there is a bit of an agreement with it as well as I didn't see any qualifiers in tweets that I was completely crazy.  Anyway, here are just a few dissenter comments:

“This article would be funny if it wasn't so sad...you can't do *anything* in IT (especially innovate) without standing on the shoulders of some standard.” – John O

“Show me data management without standards and good process to review and update them and I'll show you the mortgage crisis which developed during 2007.” – Jim F 

“This article is alarmingly naive, detrimental, and counterproductive. Let me count the ways…” – Cynthia H

"No control leads to caos... I would be amused to watch the reaction of the ISO engineer while reading this article :)." - Eduardo G  (I would too!)

After wiping the rotten tomatoes from my face from that, here are some points made that get to the nuance I was hoping to create a discussion on:

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Don't Establish Data Management Standards

Michele Goetz

A recent survey of Enterprise Architects showed a lack of standards for data management.* Best practices has always been about the creation of standards for IT, which would lead us to think that lack of standards for data management is a gap.

Not so fast.

Standards can help control cost. Standards can help reduce complexity. But, in an age when a data management architecture needs to flex and meet the business need for agility, standards are a barrier. The emphasis on standards is what keeps IT in a mode of constant foundation building, playing the role of deli counter, and focused on cost management.

In contrast, when companies throw off the straight jacket of data management standards the are no longer challenged by the foundation. These organizations are challenged by ceilings. Top performing organizations, those that have had annual growth above 15%, are working to keep the dam open and letting more data in and managing more variety. They are pushing the envelope on the technology that is available.

Think about this. Overall, organizations have made similar data management technology purchases. What has separated top performers from the rest of organizations is by not being constrained. Top performers maximize and master the technology they invest in. They are now better positioned to do more, expand their architecture, and ultimately grow data value. For big data, they have or are getting ready to step out of the sandbox. Other organizations have not seen enough value to invest more. They are in the sand trap.

Standards can help structure decisions and strategy, but they should never be barriers to innovation.

 

*203 Enterprise Architecture Professionals, State of Enterprise Architecture Global Survey Month,2012

**Top performer organization analysis based on data from Forrsights Strategy Spotlight BI And Big Data, Q4 2012

Don't Establish Data Management Standards

Michele Goetz

A recent survey of Enterprise Architects showed a lack of standards for data management.* Best practices has always been about the creation of standards for IT, which would lead us to think that lack of standards for data management is a gap.

Not so fast.

Standards can help control cost. Standards can help reduce complexity. But, in an age when a data management architecture needs to flex and meet the business need for agility, standards are a barrier. The emphasis on standards is what keeps IT in a mode of constant foundation building, playing the role of deli counter, and focused on cost management.

In contrast, when companies throw off the straight jacket of data management standards the are no longer challenged by the foundation. These organizations are challenged by ceilings. Top performing organizations, those that have had annual growth above 15%, are working to keep the dam open and letting more data in and managing more variety. They are pushing the envelope on the technology that is available.

Think about this. Overall, organizations have made similar data management technology purchases. What has separated top performers from the rest of organizations is by not being constrained. Top performers maximize and master the technology they invest in. They are now better positioned to do more, expand their architecture, and ultimately grow data value. For big data, they have or are getting ready to step out of the sandbox. Other organizations have not seen enough value to invest more. They are in the sand trap.

Standards can help structure decisions and strategy, but they should never be barriers to innovation.

 

*203 Enterprise Architecture Professionals, State of Enterprise Architecture Global Survey Month,2012

**Top performer organization analysis based on data from Forrsights Strategy Spotlight BI And Big Data, Q4 2012

Judgement Day for Data Quality

Michele Goetz

Joining in on the spirit of all the 2013 predictions, it seems that we shouldn't leave data quality out of the mix.  Data quality may not be as sexy as big data has been this past year.  The technology is mature and reliable.  The concept easy to understand.  It is also one of the few areas in data management that has a recognized and adopted framework to measure success.  (Read Malcolm Chisholm's blog on data quality dimensions) However, maturity shouldn't create complancency. Data quality still matters, a lot.

Yet, judgement day is here and data quality is at a cross roads.  It's maturity in both technology and practice is steeped in an old way of thinking about and managing data.  Data quality technology is firmly seated in the world of data warehousing and ETL.  While still a significant portion of an enterprise data managment landscape, the adoption and use in business critical applications and processes of in-memory, Hadoop, data virtualization, streams, etc means that more and more data is bypassing the traditional platform.

The options to manage data quality are expanding, but not necessarily in a way that ensures that data can be trusted or complies with data policies.  Where data quality tools have provided value is in the ability to have a workbench to centrally monitor, create and manage data quality processes and rules.  They created sanity where ETL spaghetti created chaos and uncertainty.  Today, this value proposition has diminished as data virtualization, Hadoop processes, and data appliances create and persist new data quality silos.  To this, these data quality silos often do not have the monitoring and measurement to govern data.  In the end, do we have data quality?  Or, are we back where we started from?

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How Bad Are Firms In China At Data Management?

Charlie Dai

Data management is becoming critical as organizations seek to better understand and target their customers, drive out inefficiency, and satisfy government regulations. Despite this, the maturity of data management practices at companies in China is generally poor.

I had an enlightening conversation with my colleague, senior analyst Michele Goetz, who covers all aspects of data management. She told me that in North America and Europe, data management maturity varies widely from company to company; only about 5% have mature practices and a robust data management infrastructure. Most organizations are still struggling to be agile and lack measurement, even if they already have data management platforms in place. Very few of them align adequately with their specific business or information strategy and organizational structure.

If we look at data management maturity in China, I suspect the results are even worse: that fewer than 1% of the companies are mature in terms of integrated strategy, agile execution and continuous performance measurement. Specifically:

  • The practice of data management is still in the early stages. Data management is not only about simply deploying technology like data warehousing or related middleware, but also means putting in place the strategy and architectural practice, including contextual services and metadata pattern modeling, to align with business focus. The current focus of Chinese enterprises for data management is mostly around data warehousing, master data management, and basic support for both end-to-end business processes and composite applications for top management decision-making. It’s still far from leveraging the valuable data in business processes and business analytics.
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The Kiss of Death for Data Strategy

Michele Goetz

The number one question I get from clients regarding their data strategy and data governance is, “How do I create a business case?” 

This question is the kiss of death and here is why.

You created an IT strategy that has placed emphasis on helping to optimize IT data management efforts, lower total cost of ownership and reduce cost, and focused on technical requirements to develop the platform.  There may be a nod toward helping the business by highlighting the improvement in data quality, consistency, and management of access and security in broad vague terms.  The data strategy ended up looking more like an IT plan to execute data management. 

This leaves the business asking, “So what? What is in it for me?”

Rethink your approach and think like the business:

·      Change your data strategy to a business strategy.  Recognize the strategy, objectives, and capabilities the business is looking for related to key initiatives.  Your strategy should create a vision for how data will make these business needs a reality.

·      Stop searching for the business case.  The business case should already exist based on project requests at a line of business and executive level. Use the input to identify a strategy and solution that supports these requests.

·      Avoid “shiny object syndrome”.  As you keep up with emerging technology and trends, keep these new solutions and tools in context.  There are more data integration, database, data governance, and storage options than ever before and one size does not fit all.  Leverage your research to identify the right technology for business capabilities.

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Data Quality Reboot Series For Big Data: Part 4 Big Data Governance

Michele Goetz

There was lots of feedback on the last blog (“Risk Data, Risky Business?”) that clearly indicates the divide between definitions in trust and quality. It is a great jumping off point for the next hot topic, data governance for big data.

The comment I hear most from clients, particularly when discussing big data, is, “Data governance inhibits agility.” Why be hindered by committees and bureaucracy when you want freedom to experiment and discover?

Current thinking: Data governance is freedom from risk.The stakes are high when it comes to data-intensive projects, and having the right alignment between IT and the business is crucial. Data governance has been the gold standard to establish the right roles, responsibilities, processes, and procedures to deliver trusted secure data. Success has been achieved through legislative means by enacting policies and procedures that reduce risk to the business from bad data and bad data management project implementation. Data governance was meant to keep bad things from happening.

Today’s data governance approach is important and certainly has a place in the new world of big data. When data enters the inner sanctum of an organization, management needs to be rigorous.

Yet, the challenge is that legislative data governance by nature is focused on risk avoidance. Often this model is still IT led. This holds progress back as the business may be at the table, but it isn’t bought in. This is evidenced by committee and project management style data governance programs focused on ownership, scope, and timelines. All this management and process takes time and stifles experimentation and growth.

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How To Partner With Data Quality Pros To Deliver Better Customer Service Experiences

Kate Leggett

Customer service leaders know that a good customer experience has a quantifiable impact on revenue, as measured by increased rates of repurchase, increased recommendations, and decreased willingness to defect from a brand. They also conceptually understand that clean data is important, but many can’t make the connection between how master data management and data quality investments directly improve customer service metrics. This means that IT initiates data projects more than two-thirds of the time, while data projects that directly affect customer service processes rarely get funded.

 What needs to happen is that customer service leaders have to partner with data management pros — often working within IT — to reframe the conversation. Historically, IT organizations would attempt to drive technology investments with the ambiguous goal of “cleaning dirty customer data” within CRM, customer service, and other applications. Instead of this approach, this team must articulate the impact that poor-quality data has on critical business and customer-facing processes.

To do this, start by taking an inventory of the quality of data that is currently available:

  • Chart the customer service processes that are followed by customer service agents. 80% of customer calls can be attributed to 20% of the issues handled.
  • Understand what customer, product, order, and past customer interaction data are needed to support these processes.
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Unlock The Value Of Your Data With Azure DataMarket

James Staten

If the next eBay blasts onto the scene but no one sees it happen, does it make a sound? Bob Muglia, in his keynote yesterday at the Microsoft Professional Developers Conference, announced a slew of enhancements for the Windows Azure cloud platform but glossed over a new feature that may turn out to be more valuable to your business than the entire platform-as-a-service (PaaS) market. That feature (so poorly positioned as an “aisle” in the Windows Azure Marketplace) is Azure DataMarket, the former Project Dallas. The basics of this offering are pretty underwhelming – it’s a place where data sets can be stored and accessed, much like Public Data Sets on Amazon Web Services and those hosted by Google labs. But what makes Microsoft’s offering different is the mechanisms around these data sets that make access and monetization far easier.

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Pros and cons of using a vendor provided analytical data model in your BI implementation

Boris Evelson

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:

Pros:

  • 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

Cons

 

  • 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
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