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Even though purists don’t like the term, "Big Data" is a reality that’s coming to an enterprise near you. Big data is different from traditional data warehousing and business analytics initiatives.
Traditional DW/BI analytics systems tend to be highly centralized with infrastructure built around a 'data temple' consisting of an RDBMS (typically from Oracle, IBM/DB2 or Microsoft SQL Server), high performance storage from EMC (the DW/BI roll-your-own market leader), IBM or HP and analytics software from the likes of Cognos, SAS, Microstrategy, etc. Lately, the market has been disrupted by Oracle with the Exadata appliance that has brought single SKU simplicity to the marketplace
Big data apps on the other hand leverage a diverse set of semi-unstructured or unstructured data types that are disbursed on the Internet. Data scientists perform mathematical, statistical, and data hacking operations to mash up data and create enormous databases typically accessible over the Internet. Increasingly big data applications are becoming a source of competitive value for enterprises as firms monetize information by building data products and services. Going forward, the exploitation of data will become of increasing importance to enterprises.
CIOs need a big data strategy and an answer to the question: "What is our organization doing around so-called big data." The questions CIOs should be asking are:
- What is the potential value of data to my organization?
- Beyond traditional data warehousing, which opportunities exist for monetizing new data models?
- How can data be best exploited, and how should we go about leveraging the new data realities?
- What skill sets do we need to compete effectively?
- Which partnerships should we develop with the line-of-business?
- Which ISVs are best positioned in our market space?
Despite uncertainty within many organizations, several things are clear:
- Big data requires new thinking – it’s not an simply an extension of traditional DW/BI.
- Big data requires new skill sets revolving around math geeks and data scientists as well as business skills that can construct revenue models around data.
- Big data is largely unstructured or semi-structured.
- Big data requires new architectures (e.g. MPP, Hadoop, new database approaches new tools, etc.)
- Big data presents monetization opportunities that are specific to many industries and not necessarily cross industry.
In addition, major opportunities exist to partner with customer care professionals and ask new questions that previously couldn’t be answered (e.g. what are my customers doing, where are they going, what’s the experience like, and how can it be improved).
While traditional DW efforts will not die, they have often failed to deliver the predictive monetization value that many hoped for. Big data applications promise to deliver this value as vast Internet data reservoirs are tapped and made available through open APIs, new architectures, and emergent business models.
Action Item: While many unknowns exist around so-called big data, CIOs need to position their organizations to take advantage of the new data reality by developing a strategy around big data. CIOs in data-rich industries should organize an elite team consisting of data scientists, programmers, and business professionals that can monetize data. This team should be tasked with developing a comprehensive data strategy and identifying an industry-specific ecosystem that can evolve with both internal and external partners.