THE PAST: Analytics has come a long way in last two decades. It started as back of the envelope calculations thought of as business secrets of top level cigar smoking business executives accumulated month by month year by year from experience and data they have watched, analyzed and inferences drawn. Then came the ubiquitous RDBMS based web applications that made data entry job of end users rather than back end employees. This generated enormous amounts of data over the years. Hence the birth of analytics technologies and frameworks.
THE ANALYTICS TECHNOLOGY LANDSCAPE
Since the bulk of data resides in RDBMSes so it was imperative that the products and tools that will help organizations leverage this data to predict upcoming opportunities and what customers want, will be very compatible and coherent with these technologies. This spawned a whole analytics software industry with products like Vertica, Netezza , Teradatafor large scale storage. This also gave birth to few other families of products. One such conspicuous family is ETL tools like Ascential DataStage, Informatica, AbInitio and Talend. Add to this some other products that help leverage these by going above and beyond the basic analytics features that come standard with bulk storage. This category includes Cognos,Microstrategy, SAP’s Analytics products, Oracle OBIEE, SAS.
As these products, provide features on top of other products and add value to existing IT investments, they tend to command high price tag. Professional services for these also get priced at premium rates. Therefore, the inherent cost prohibitive nature of these products made them only available to top 1% of Fortune 500 companies. The lower rung management was left with same old archaic ways of predicting marketing, planning and forecasting, figuring out operational efficiencies and all such services that analytics tools help with.
THE BIG DATA IMPACT ON ANALYTICS ECOSYSTEM
Then came a new generation of tools and technologies with Google and the likes leading the pack. Google’s Big Table started off with the need to store massive amounts of data i.e. crawling whole of webs content,indexing and tagging it. However the real value was realized when Google performed analytics on this data to figure out what a user is looking for and display relevant advertisements. Today Google uses Big Table, Facebook uses Cassandra and LinkedIn uses Kafka with one common requirement of crunching massive amounts of data and using it to suit their business model and needs. These frameworks are open-sourced and available freely. Tens and hundreds of developer communities and organizations are constantly contributing to these frameworks-their tooling and missing functionalities. One commonality that all these share is that they are device independent and run well on any commodity hardware.They are also inherently cluster-able therefore scaling up implies buying more commodity hardware and stacking up the data centers as your market, revenue and user base grows bigger and bigger.
THE OTHER GAME CHANGING ADVANCEMENTS
Coupled with some other advances all this comes together as a game changing proposition. Analytics is not anymore the domain of the top 1% of management that sits under glass ceiling. It is as commoditized as the hardware that these frameworks run on. Add to that the internet bandwidth becoming more and more fast and cheap by every passing day and Gigabit speeds becoming a standard. Now mid-sized companies and middle management of big companies can afford to leverage latest analytics technologies that were thought of elitist tools until recently. Managers on shop floors or in the field can get this information on the go on their tablets and smartphones while driving on the streets. The affordability goes a few more notches up if you factor in the cloud compatibility of these technologies that brings down cost of ownership drastically down and ‘per use’ based and more importantly makes it effectively measurable. Now a mid-sized company can perform a round of analytics using cloud based infrastructure and calculate the dollars spent versus dollars made or saved and see for themselves what worth it is. Moreover traditional analytics tools tend to be very offline and batch oriented and the vendors of these technologies successfully sold this limitation of high lead times to get the results to their clients. Lot of new generation frameworks are challenging and successfully countering the batch and offline assumption and providing value that previous generation of million dollar tools failed to provide. Now businesses have to take a call on whether to continue on the same route or take a detour using the new toll-free highway to analytics.
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