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Posts tagged ‘Performance management’

5
Mar

ROI on Analytics – Now We Have Numbers


Return on InvestmentA recent study by the Nucleus Research says that Analytics pays back $10.66 for every dollar spent. The study is based on data from 60 case studies and relates to investments in Business Intelligence, Performance Management and predictive analytics. Not surprising are the areas where they saw ROI increase – revenue, gross margin and expenses.

Enterprises have used various metrics to track the effectiveness of Business Analytics. Cycle Time to Information (CTI) is a metric that measures the elapsed time between the occurrence of a significant event and the time this information is available to a decision maker who has to act on that information. Cycle Time to Action (CTA) is variation of this metric which measures the elapsed time to act on information after an event occurs.  These metrics are useful to track the efficiency of a Business Analytics infrastructure and the elimination of manual processes to increase productivity. As the volume of data increases in an enterprise, automation in data management will become more complex in the future. Read more »

2
May

BI, Analytics, Reporting Center of Excellence (CoE)


Everyone has data, but the more elusive goal is getting value out of that data  The growing challenge in corporations is how to organize for “data as a platform.” What is the right organizational structure that will help monetize data?

John Wanamaker, considered a pioneer in modern advertising, said: “Half the money I spend on advertising is wasted; the problem is I don’t know which half.” Today, we can say the same of enterprise investment in business intelligence (BI), analytics, and big data.

Even after doing their best for over 20 years to build centralized, scalable information architecture, I found that only a small percentage of organizations’ data is actually converted to useful information in time to leverage it for better insight and decisions.

At both strategic and tactical levels, much of this gap can be explained by the fundamental disconnect in goals, objectives, priorities, and methods between IT professionals and the business users they should ideally serve.

The other challenge facing leadership is the rapid evolution of the data platform (see below.)  How do you create strategies that adapt to a changing landscape?

Evolution of Data Platform

Leadership Challenge

How do you become a world-class data-driven firm? What portfolio of projects do you execute to mature the capabilities?

If you’re an executive, manager, or team leader, one of your toughest responsibilities is managing and organizing your BI, Reporting or Analytics initiative. While the nuances – skillsets, toolsets and datasets — are different for each initiative, the fundamentals of managing, organizing and structuring are pretty much the same.

Almost every Fortune 1000 company’s management is increasingly focused on monetizing small data, big data or fast data, and how to gain a real-time competitive edge from their information. How can firms achieve positive returns on their analytic investments by taking advantage of the growing amounts of data?

So what’s the right organizational model that will help them achieve the “ten second advantage”? Competency Centers, Centers of excellence (CoE) or Shared Services models are execution models to enable the corporate or strategic vision to create an enterprise that uses data and analytics for business value.

BI CoE Slide

The goal of every World-class CoE is the same – enable the right combination of toolsets, skillsets, mindsets and datasets for better, faster, cheaper and more repeatable analytics, reporting or platform development.

Evolution of BI/Reporting/Analytics

  • Data is Growing Faster than Budgets
  • Demand is Growing, Speed to Insight is Crucial
  • Modifying large, existing applications is NOT the path forward.
  • Skills are lagging.. New tooling

As a result, Enterprise BI and Analytics strategies need to evolve.  The evolution tends to happen in 3 phases:

  • Department Solutions – Many companies deploy Analytics (and BI) applications as departmental solutions, and in the process, accumulate a large collection of disparate BI technologies – SAP Business Objects, IBM Cognos, Microstrategy, Oracle OBIEE, Microsoft, Qlikview, Tableau, Spotfire etc. – as a result. Each distinct technology supported a specific user population and database, within a well-defined “island of analytics.” At first, these dept islands satisfied the initial needs of the business, but early success in departmental deployment sowed the seeds for new problems as the applications grew.
  • Successful applications and platforms always expand. The second phase of Analytics (and BI) is where there is tremendous growth and  platform solutions are longer isolated islands. Instead, they overlap in user populations, data access, and analytic coverage. As a result, organizations are now faced with an untenable situation. The enterprise is getting conflicting versions of the truth through the multiple disparate BI systems, and there is no way to harmonize them without an extraordinary ongoing manual effort of synchronization, validation and quality checks. Equally problematic is the fact that business users are forced to use many different BI tools depending on what data they want.
  • The third phase of Analytics (and BI)  is one where the executives had enough. They simply make a decision to rationalize to a single platform or a centralized model that is sold as a “magic nirvana” solution…delivers one version of the truth (golden source of data) to all people across the enterprise. It can access all of the data, administer all of the people, eliminate repetitive data access, reduce the administrative effort, and reduce the time to deploy new BI applications.

“Time to decisions, scope of decisions, disconnected toolsets and cost of decisions” is deemed unacceptable within & across functional areas.  This typically drives a new phase… centralized BI, Reporting or Analytics CoE.

For example, at a Fortune 500 company, costly self-service environment, static reports, departmental solutions and other issues (shown below) forced them to re-think and re-engineer their enterprise BI solution. The firm set new target objectives…(1) Shorter time to insights; (2) Greater leverage for analytics team; (3) Accelerated product innovation and (4) 20% reduction in BI support costs.

While centralization of BI, Reporting and Analytics can enable organizations to reduce their IT delivery costs by up to 40%. However, a failure to align the level of BI, Reporting and Analytics centralization closely to long-term business and IT strategic goals and to manage the transition to centralized delivery carefully can not only erode expected savings from centralization, it can increase the cost of delivering IT services by up to 30-45% compared to a pre-centralization baseline.  This where good management can make a big difference.

BusinessChallengeFortune500

BI CoE Elements for Faster, Better, Cheaper Execution

BI CoE (could be Analytics CoE,  Big Data CoE or Integration CoE) is an organizing mechanism to align People,  Process,  Technology and  Culture.  The target benefits include:

  • Better collaboration between Business and IT
  • Increased adoption and use of BI and Analytics in the lines of business.
  • Better data management, quality and reporting
  • Cost savings from eliminating redundant functions

CoE elements include:

ElementsofCoE

Read more »