We are today standing at the thores of how to apply Big Data into the enterprise. There are several pathways to start the journey and reach different levels of maturity for the implementation of Big Data. In developing several of these implementations with different companies across the globe, we have created a series of courses that will be presented at TDWI Chicago in May 2013. To give you a perspective, there are courses that target how to build a business case for Big Data to Advanced Analytics on Big Data. As an incentive, TDWI has graciously offered a special discount to attendees and the details are available here

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Looking forward to seeing you at the event.

If you read books on strategy and management as subjects, the end rule always focuses on the term ROI and the time to realize the same. There is nothing wrong in estimating the value of any investment, the market opportunity, risks and time to recover profits, but the situation changes drastically when you apply the same techniques to an organization on all the programs and projects in-house, often resulting in chaotic situations and emotional upheavals.

The main idea behind applying ROI based techniques for any in-house program is to provide management with a roadmap on the value of the investments in technology and the business benefit it will bring about. The one area where we have traditionally struggled to provide a clear and concise point of view is the area of "data", ironically while the same has been classified and touted as an enterprise asset for many years. How does one really apply ROI to data strategy?

The techniques of using data quality and integrated data architectures help in building a business case for data strategy, but does not clearly articulate the ROI as it does not tie the business outcomes to the data strategies used in the organization. In order to measure the ROI on data strategy, we need to employ a combination of
 

• projected or predicted ROI from all data programs
• realized increase in business initiatives
• increase or decrease in profitability
• measured customer sentiment

By creating a mashup  of the different pieces, we can create a co-relation on the initiatives of data strategy and the ROI, with a measurement of true impact on the business. This type of value driven mechanism is needed to realize the true ROI on data strategy.

Monetization from the data strategy efforts can be traced with this method and clearly documented along with trends and timelines. This is not a simple exercise and needs to be implemented with acceptable margin of error for the first few iterations till a maturity model can be established.

Once we have this type of a model, every program of data strategy can be tied to a measurable value and you can predict the tangible ROI and the timeline for the realization with a higher degree of confidence. This type of practice exists in many organizations albeit on a tribal scale and it needs to be enabled and empowered to become an enterprise level strategy.

This type of approach uses all the soft costs and the tangible performance results of the business together and hence it is to be treated with utmost security and governance to protect the competitive advantage of the business. Watch for my expanded whitepaper in the next week on this topic.

The buzz is all about Big Data today in our industry and we all seem to be in a state if awe with this happening. Let us take a step back for a minute and pay attention to the trends that drive business and decision making - it has always been oriented towards competing on products and services since the dawn of time. The advent of web 2.0 and the long tail model combined with the explosion of social media transformed this trend from products to customers.

Businesses started looking at customer sentiments and behaviors closely and started identifying what drives the customer to be loyal to them. Beyond just customer loyalty the trends that have started driving the business is Customer Centricity and associated transformations in business. This is where Big Data plays an important role within enterprises today and the underlying need to integrate and explore the data and insights from the same.

While the buzz from Big Data may stay or die after a period of time, the new trends and directions that it has created for businesses to follow will be the future trend.

I had written in my prior post that Big Data Appliances are coming around the corner and it looks like deja vu all over again.

Teradata today announced the new Teradata Aster Big Data Appliance, and yes as the name spells it all it is the first integration product from the stables combining not only Aster and its legendary platform with the bells and whistles of Teradata platform in terms of management tools and hardware capabilities and combining the Hadoop integration with Hortonworks

The system in my opinion is a perfect combination of power and ruggedness with a lot of finesse and it differs from competitive announcements on these grounds

1. Aster's patented Sql-H and its legendary scalability architecture
2. 50 plus analytical functions designed to work on large data sets
3.  SQL-MapReduce combining SQL and MapReduce, which was an Aster core strength in 2009
4. Teradata Viewpoint - a well known and tested management tool for the platform - extended to include Aster and Hadoop
5. Teradata TVI a very sophisticated hardware support and failure prevention software
6. Infiniband network interconnect - makes ultra-high-performance connectivity between Aster and Hadoop, as well as scalability, a non-issue

For those of you who have been around these platforms, it definitely is not a me-too solution or taking existing solutions and re-integrating or re-branding them. Neither is the platform configuration a custom build. From a CIO's perspective this is where the Teradata Aster Big Data Appliance makes a strong business case, it brings all proven and tested technologies in a appliance footprint with all the configurations required to handle the onslaught of Big Data.

As a Big Data practitioner and a Data Warehouse evangelist, what truly is a future think architecture from my perspective is the "unified architecture". This is where the rubber meets the road in my opinion and I have discussed a similar solution in any seminar or discussions that I continue to have on Big Data.



What this architecture does for you as a user is creating two platforms at a the same time - one for exploration and mining purposes and the other for analytics and management reporting. You can push workloads across the different architectures here and leverage the power of all the pieces of the infrastructure. With the right approach and solution architectures, enterprises can take a giant leap forward for the Big Data journey on these type of platforms.

The engineering efforts of Teradata, Aster and HortonWorks speaks for itself in terms of performance tests. I look forward to more testing and benchmarking results on large data volumes.

While all the technology announcements till date have been very innovation focused, this one makes a business case at the very introduction itself and that is what gets a business executives attention. The passion and commitment of all the teams involved shows off in the appliance performance from current tests and recorded benchmarks.

This is not the last of the appliances, there are more to come and the users will have a greater choice. If I were to compare anything in the Auto industry today with these appliances, Teradata Aster Big Data Appliance is like the next generation of Prius with more bells and whistles, while IBM Pure is like a custom built souped up supercar. Both have their enthusiasts and loyalists, while both have been able to address different user needs.

Whichever is the direction one chooses, this is the dream come true era for many solution architects, DBA's and CIO's.

This year will be one to remember in the history of Data Warehousing. About an year ago, the frenzy of Big Data had already numbed our senses and we were wondering how to tame the beast? is Hadoop the only known savior. An year later, we are seeing a new lineup of infrastructure and technology warriors from the known stables of Data Warehouse and Database vendors - Oracle Big Data Appliance; IBM BigInsights platform; ParAccel; Kognitio; EMC Greenplum and the saga goes on.

What interests me is the legacy that is being carried through, as we move on from the Data Warehouse Appliance to Big Data Appliances. The original promise of the Data Warehouse Appliance was a self contained box of hardware, software and API built to handle the rigors of the Data Warehouse, and the Big Data Appliances will be a self contained box configured to handle the demands of Big Data. In the newer situation the issue will be where does all of this go into the ecosystem and how does one handle all the workload. Well that is for the architects to figure over time.  Of course at the time of this writing, we still have more to come our way in all probability, never say we are done with anything in technology.

Innovation thrives and exists.