Achieving Analytics Maturity: 3 Tips from the experts

What does it take to achieve analytics maturity?  Earlier this week, Dave Stodder and I hosted a webcast with a panel of vendor experts from Cloudera, Microstrategy, and Tableau.  These three companies are all sponsors of the Analytics Maturity Model; an analytics assessment tool that measures where your organization stands relative to its peers in terms of analytics maturity.

There were many good points made during the discussion.  A few particularly caught my attention, because they focused on the organizational aspects of analytics maturity, which are often the most daunting.

Crawl, Walk, Run:  TJ Laher, from Cloudera, pointed out that their customers often a crawl, walk, and then run to analytics. I’ve said before that there is no silver bullet for analytics.  TJ stressed the need for organizations to have a clear vision of strategic objectives and to start off with some early projects that might take place over a six month time frame.   He spoke about going deep with the use cases that you have and then becoming more advanced, such as in bringing in new data types. Cloudera has observed that success in these early projects often helps to facilitate the walking and then, ultimately the running (i.e., becoming more sophisticated) with analytics.

Short term victories have long term implications:  Vijay Anand from Microstategy also touched upon idea of early wins and pointed out that these can have long term implications.  He pointed out that it is important to think about these early victories in terms of what is down the road.  For instance, say the business implements a quick BI solution.  That’s great.  However,  business and IT need to work together to build a certified environment to avoid conflicting and non-standardized information.  It is important to think it through.

IT builds the car and business drives it.  Ian Coe, from Tableau, also talked about IT and the business working together.  He said that organizations achieve success and become mature when teams work together collaboratively on a number of prototypes using an Agile approach.   The over the wall, waterfall approach used by IT in the past won’t cut it because moving forward with analytics involves people and rapidly changing questions.  Tableau believes that the ideal model for empowering users involves a self-service BI approach. Business people are responsible for doing analysis. IT is responsible for managing and securing data.  This elevates IT from the role of dashboard factory to architect and steward of the company’s assets.   IT can work in quick cycles to give business what they need and check in with business regularly.

Of course, each expert came to the discussion table with their own point of view.  And, these are just some of the insights that the panel provided.  The webcast is available on demand.   I encourage you to listen to it and, of course, take the assessment!

AMMA_Stages

Two Big Data Resources Worth Exploring

It’s a good day.  Our new book, Big Data for Dummies, is being released today and I’m busy working on a Big Data Analytics maturity model at TDWI with Krish Krishnan.  Krish, a faculty member at TDWI, is actually presenting some of the model at the TDWI World Conference:  Big Data Tipping Point taking place during the first week of May (see sidebar).  I would encourage people to attend, even if you aren’t that far along in your big data deployments.  TDWI has terrific courses in all aspects of information management and we understand that most companies will need to leverage their existing infrastructure to support big data initiatives.  In fact the title of this World conference is, “Preparing for the Practical Realities of Big Data.”   Check it out.

Back to the book.  Here’s a look at the Introduction!  Enjoy!

 

Best Practices on the road to Enterprise-wide MDM

I recently had an interesting discussion with Ravi Shankar, Director of Product Marketing at Siperian, about emerging best practices for enterprise-wide MDM initiatives.  Siperian provides MDM hubs for large companies across a number of industries.  Now, I have noted before that MDM is a complex undertaking that needs to be thought about at a strategic level.  An enterprise-wide MDM deployment is not going to happen all at once.  Here are three points related to the idea of strategic enterprise-wide MDM that I found worth noting:

Business-Centric vs. Entity-Centric MDM

Siperian is seeing a growing number of companies entering into MDM in  response to a particular business solution area and asking what entities are needed for that solution, rather than the other way around.  Let’s call this a business-centric approach to MDM rather than an entity-centric approach.  The entity-centric approach addresses entities- the products data, account data, the contracts, etc. – one at a time.  It is technical in nature.  The business-centric approach addresses a specific business problem – such as processing benefits and payroll or processing sales leads – and examines all of the entities needed to support the initiative.  The business-centric approach to MDM provides a complete solution to the business problem and illustrates the value of MDM in a tangible way.

A solution-based evolutionary approach to enterprise-wide MDM

Companies viewing MDM at a strategic level are adopting a well-planned evolutionary approach.  This might consist of starting with a single MDM implementation for a particular business solution, with a single hub, multiple entities, certain architectural style (coexistence, transactional, or registry) and a mix of operational or analytical usages.  As a company develops more business solutions, each with its own hub, with multiple, potentially overlapping entities, and perhaps different architectural and usage styles these solutions need to be linked together.  For example, a company might have separate masters, with some overlapping entities, one for a certain business solution and another for a different business solution.  Siperian is seeing companies use a federated MDM approach to link these hubs together.

Local to enterprise-level Data Governance

Data governance is obviously a huge part of the development effort.  In the first hub, usually local data governance will suffice.  However, once multiple hubs are deployed, each utilizing some of the same entities, a cross-functional data governance approach is required.  This can involve local data stewards working cross-functionally with an enterprise data governance council.

Of course, the business side of the house needs to be involved with all of this.  They need to own the business solution.  They are central to the governance effort.  They need to fund the federated hub.  Once divisions in a company can get past the politics and perceived bureaucracy of MDM an enterprise-wide MDM deployment is doable, as evidenced by the growing number of companies that have actually accomplished this.

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