Analytics & Optimization 6 Best Practices for Maximizing Big Data Value


 survey  released last month, indicated that Hadoop adoption is facing challenges. Specifically, the vast majority of respondents had no current plans to invest in Hadoop due to “…sizable challenges around business value and skills”. Since Hadoop is the leading tool for Big Data, this points to a bigger problem in overall Big Data adoption.  Finding resources with the right analytic skills is a difficult challenge that is being targeted by a new generation of business friendly Big Data tools.  Getting business value is a more fundamental issue.  For many organizations that I’ve talked to, the plan for getting business value from Big Data is simple: get a deeper understanding of how customers behave, and then leverage that knowledge to improve customer satisfaction and increase business profitability.  That can be easier said than done, but reviewing some of the emerging best practices seems like a good place to start.

Maximizing Big Data’s value comes down to doing six things well:

  1. Start with a business problem in mind: Wandering through huge amounts of data with Hadoop and other advanced analytic tools can be lots of fun for data scientists, but it can also be a huge waste of time and resources if the results do not translate into something that can be applied to solve real world business problems. Working with business experts to understand their challenges and opportunities for improvement is a key ingredient for successful projects.  Focusing on a specific business problem makes it easier to identify useful data sources and choose appropriate tools and techniques.  It also sets you up for the next step…
  2. Look ahead to how you will put your insights into practice: To achieve real business value you have to be able to operationalize the results of your analysis. Although this sounds obvious, far too many projects are left gathering dust on the shelf because it is too hard to incorporate their findings into the business activities that would benefit from them.  Data that looks wonderful in the lab may not be available, or may be too expensive to get at the time you need it for use in day-to-day business operations.  Industry regulations can also have a huge influence over where and how your data can be used.
  3. Take advantage of the latest analytic innovations: Innovations in Business Intelligence and Business Analytics are transforming how businesses get value from their customer data.  This is causing a shift from traditional approaches that provide periodic snapshots in the form of descriptive reports and historical dashboards, to systems that continuously analyze incoming data to provide prescriptive insights that are actionable in real-time. Big data tools and infrastructure are making it faster and easier to apply machine learning techniques to explore huge datasets that include a wide variety of structured and unstructured data.
  4. Embrace analytic diversity: R, Python, Hive, Groovy, Scala, MATLAB, SQL, SAS; which one is your favorite?  One of the side effects of the exploding world of analytic innovation is that taking advantage of the latest techniques often requires learning a new set of tools.  Waiting for your favorite analytic tool vendor to catch up and provide an integrated solution isn’t usually an option.   Leading analytic teams will inevitably need to use multiple tools to support their business needs, so the best approach is to embrace diversity and create a flexible infrastructure that can operationalize models authored by a wide range of tools.  Getting multiple types of analytic models to work together in a robust production environment can be a significant challenge. However, modern decision management systems like the FICO® Decision Management Platform simplify the task by supporting extensible libraries and taking advantage of web services and standards such as the Predictive Modeling Markup Language (PMML) to combine analytic services and business rules into cohesive decisioning applications.
  5. Leverage the Cloud and productivity platforms: Creating big data analytics no longer requires making a huge investment in expensive infrastructure and specialized skills.  By running your analytic projects in the cloud you can let a dedicated third party handle the underlying systems and services while you focus on the business problem at hand. You can rent out just the capacity and services you need, at a fraction of the cost of implementing your own.
  6. Give Control to the Business Experts: The final ingredient for getting value from your big data analytics is also the most important one.  The greatest value comes from giving business experts new insights that they can quickly turn into differentiating strategies and actions that will delight customers and shareholders alike.  Interactive and highly visual dashboards and reports can provide information that helps business experts to refine and evolve high-performance strategies.  Standard decision management components such as business rules authoring services can make it faster and easier for experts to incorporate new models and insights into their business rules and policies.  Simulation and data visualizations can also speed the approval time for implementing new models and strategies by making it easier to understand and explore their potential impact.

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