Bring Big Data Into Your Business Intelligence Village

**Jeff Weidner is a Big Data Practitioner and partner of JW Analytical Advisors. He has decades of experience being a trusted advisor towards transforming data into actionable insights. Applying Business Intelligence and behavioral statistical analysis to a variety of situations including Digital Marketing Intelligence his innovative approaches are tempered by his experience at Dell as Director of Marketing Information Governance.**


I want to see more analytical partnerships in organizations.

Right now the demand to create valuable and actionable assets from data is at an all-time high. Big data initiatives, following Machine Learning or Streaming Data, are blooming all through organizations. However, these initiatives many times may overlook, bolt on or separately design how Business Intelligence operates because planners see the citizenship differently.

A key opportunity for these companies is to capitalize on an organization’s mind share. And the great news is that Business Intelligence solutions are keeping pace. These solutions want to be part of a shared ecosystem that allows BI teams to create diagnostic and predictive insights, and then put them into production.

To address this, organizations need to demystify Big Data, find a balance between the roles, technologies and processes involved, and bring together the Data Village.

Columbo Meets Frankenstein

Take, for instance, the last time you were called for an executive request and the resulting data management overhead that was created.

For me, as a lead for Behavioral Statistics, I am challenged to find deeper customer insights and the justification for action on many fronts. The majority of data analytics projects now focus on engaging an aspect of the customer experience beyond operational efficiencies.

This ‘new’ marketing analysis must not only tell a story that resonates with decision makers — It must also demonstrate an improvement to a customer’s satisfaction, while ensuring a strong return on the organization’s investment in the technology and cultural change required to meet that goal.

Such a request starts with the tactical scramble to re-assemble a deliverable that fits into the current narrative. Likely, there were already dashboards or reports that told (part of) the story and it should be a straightforward request. However, inevitably, the requestor needs just one more data point or slice that was not planned.

First, any new data being brought in will beg questions from your team about trust in the data — because yours is the only report and data mart out there, right? Data Governance initiatives, encouraged by the scale in which companies are invested in Big Data, are driving transparency for these assets.

Second, the time assembling the data is also a factor. Your Enterprise Warehouse does not have this data and your local BI Analytical Mart was likely designed to answer certain “knowns.” For timeliness, your team plans to go pull down and bolt on these new data points. Big Data technologies can help to make the process repeatable and more agile by opening up rich datasets that may be used by the Data Engineers.

Third, questions create more questions. The stakeholder will likely ask for the justification that requires you to dig deeper into the insight. What if you find discrepancies within the results provided by your vendor, consultancy and your internal team? Is there money left on the table because the skill set, tools or data are not there to look at the information differently? Being able to tap into your Data Science team is one option, but being able to self-service creates that needed agility.

Three Tips to Bring Big Data Into Your Village

While there will be the balancing act of providing unlimited access to those who “need to know” and shielding information from everyone else, the benefits gained from transparency fuel an organization’s transformation:

  1. Demystify Big Data vs. Business Intelligence: BI and Big Data workers face the same challenges — lack of the right data, its context and the ability to make it repeatable. They are all looking for that chance to unearth valuable insights. They may utilize different tools, work in different data ecosystems and use different language when discussing the business outcomes. Organize sessions where these teams can share.
  2. Create a “Public Works” Team: Create a cross-functional and cross-organizational team whose function is to consolidate, expose, and generally curate data for use by BI, Analyst and Scientist alike. This will save time for all teams in the long run, as a tremendous amount of money is often spent on rebuilding data that may already exist.
  3. Finally, Plan to Integrate Your BI Ecosystem and Its Workflow With the ‘Big Data’ Space: One way to start is by empowering teams with tools and processes to self-service acquire, explore, prepare and build shared data models from diverse datasets.

A growing number of organizations are already able to rapidly uncover new insights and trends with these simple steps; start with your village.


Fueling your BI analysts with big data simply should not be hard. Join Andrew Brust and myself for a live webinar on December 13th where we’ll discuss how to effectively marry your big data with your BI community. The session will examine specific steps from trusted practitioners to optimally fuel your BI users with big data. Click here to register: Power Your BI with Big Data.

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