As larger data sets start to take root across various industries, it is going to be important to put those “big-data” results into a more manageable picture for end users and analysts.  Many of the existing “big-data” end users are already familiar with the data sets and how they wish to look at those data sets.

However, the true value of “big-data” or analytics on “big-data” is going to be presenting the information to the end user who may still be thinking about analytics in “small-data” ( … or relatively small data… ) terms.

For example, new “big-data” analytics provides a “richness” of information and an increase of the dimensions that “small-data” systems cannot match.  Yet, many users in marketing or product management may not understand how to make the leap from “big-data” aggregates to “big-data” detail because they don’t have the context of the “big-data” detail(s) they are looking at.

Mixing and Match with Big-Data

The twin challenge associated with the ability to handle and analyze “big-data” is the ability to put that analysis into context.  “Big-data” often refers to senor, geographic or application data.  However, not many people in end user/analyst communities have the ability make the leap from those “big-data” details to an end “so what picture?”.

This week Tableau announced the next edition to their business intelligence / data visualization product line – Tableau 6 – which supports the ability to “blend” data sets for end user visualizations that will tell the story that marketing and product management will understand and have that “AHA!” moment.  While the data visualization is nothing “new”, the ability to perform with “big-data” data sets will be the key aspect.  If the visualization takes too long, the marketing analysts and product management teams will lose interest and use less detailed analysis tools. 

Telecom Take

As telecom data rockets further and further for social media, location based services and overall smartphone usage; “big-data” is going to hit head long into telecom BI/DW teams.  And while those teams are struggling with the ingestion of the data, end users are going to demand analytics and visualization tools that don’t hold back their “day jobs” from being completed…

Using data visualization tools, like Tableau’s new offering, will offer the ability to match the potential of the data with promise of the analysis. 

How is your telecom BI/DW team positioned to meet end user requirements for visualizing big-data? Strictly using aggregates? or big-data detail?

Post your comments below or email (John.Myers@BlueBuffaloGroup.com) / twitter (@BlueBuffaloGrp) me directly.

At this week's TDWI Conference in Orlando, the focus is on Emerging Technologies.  The Monday Keynote presentation focused on the ability of an organization to use "nimble" development practices ( ... as opposed to Agile methodologies... ) and cloud based technologies to enable quick results.

"When the CEO comes knocking..."

Kevin Rooney's keynote presentation focused the results of an effort where a company CEO wanted to understand his company's position within particular insurance markets and how to increase the company's position within the market place via customized/optimized price points.  However, many questions "loomed" over the effort:

• Could all the publically available data be used effectively?
• Was there value in effort?

Rooney developed a nimble response team within his IT organization that tackled the issues of determining if the data available/feasible and if the business model was possible.  NOTE - Rooney used a technique that Harvard Business Review has advocated for reducing the tension between existing "legacy" and breakthrough innovation teams.

Rooney also reached out to the team at Kognitio for an analytical platform that would allow for an initial "proof of value" and a minimal capital expense (capex) rollout into production as well as a powerful analytical platform to perform the types of queries required of the effort.

In this, Rooney linked a nimble development team with a power and flexible analytical engine to develop a competitive advantage application for his CEO is a timeframe that allowed his firm to capitalize on the opportunity and develop areas of competitive advantage.

Telecom Take

With the stated goal of many of the major telecoms utilize metered billing plans in the future, telecom organizations need to be flexible in their approach to understanding how those billing models will impact profits.  It will no longer be appropriate to set metered or utility plans and then 'see how they do'.  Telecoms will need to be flexible with "what if" management scenarios via either descriptive or predictive analytics to provide analysis on which plans will be profitable and which will not.

NOTE - BT has a long history of doing this type of analysis.  However recent developments relating to increased smartphone usage and the need for more flexbile pricing models will drive an increased need for this type of work. 

Powerful analytical platforms like Kognitio will be part of the solution.  However, it will be forward looking analyst organizations that make these solutions possible.  Those analyst organizations, with telecom BI/DW teams, can utilize tools within a nimble analysis cycle to implement valuable projects. 

How is your telecom organization handling flexible metered billing situations? Reactively with spreadsheets or proactively with "what if" models?

Post your comments below or email (John.Myers@BlueBuffaloGroup.com) / twitter (@BlueBuffaloGrp) me directly.