Analytics is serious business. I don’t care what buzz words are being used in today’s day’s stylized reinterpretation of the data business that we are in but analytics has indeed come a long way. Both, in the way it is done and the increasing incidence of companies doing it to stay competitive and grow. Thing is, in an odd sort of way, analytics is often done in tentative spurts of frenzy and the business of providing analytic solutions that can create a sustained analytics practice and one that is fully beneficial to the business is, well, rarely the norm in companies today.
Challenges that impede a sane analytics practice
So, let’s first answer the question of what challenges companies face in creating this competitively differentiating analytics practice on a sustained basis. But before I get into this, a quick note. Typically, in the spirit of shameless confessionals, my blog posts are prolix, long-winded affairs. In the interest of judiciously using your spare time I will be brief and if or when you may have the desire to engage in a larger relaxed conversation, reach out to me. Now, for the challenges:
First, businesses today generate vast gobs of data of all types, shapes, sizes, looks, across different times, and at various speeds. Consequently, analytics solutions need to be repositories of these diverse data. One cannot have bespoke storage solutions for each data type and confront an array of infrastructure that requires all kinds of physical and mental gymnastics just to get all the data in one place.
Second, the diversity of data alludes to the diversity of the business. For example, when customer data is collected through CRM systems, web servers, call center notes, and images it means that customers engage with the business via the store, through online portals, via the call center, and on social media. To understand the multi-channel experience we need to analyze these diverse data through multiple techniques. Unfortunately, to do that businesses have been forced to buy multiple analytic solutions, each with a unique set of algorithmic implementations and none easily interacting with the others.
Third, let’s assume that the ability and purpose of doing advanced analytics is there and well established. Now comes the challenge of purchasing a solution that can neatly fit into the budgetary constraints of the business. Not for the first time can I recall a customer who has expressed an inability to transact business with a vendor not because they do not have a desperate need for that vendor’s solution but because they are likely locked into a solution purchase that inevitably restricts their flexibility of deploying it. For example, a customer may first desire to kick the tires with a solution by purchasing a limited time cloud subscription before they are able to commit more resources to it. This is only a fair ask. Once they are successful in a minimal-risk purchase they can up the ante by buying something more substantial depending on their needs. Analytic solutions that cannot fulfill this primal customer need will fast recede as a prickly memory of the past, regardless of how good or versatile they can be.
Analytics today is often done in tentative spurts of frenzy.Tweet This
Teradata Analytics Platform
Now that I have outlined the challenges, how about providing a rational fix for these? Fortunately, and not too coincidentally, this is a pleasant enough occasion for me to introduce the Teradata Analytics Platform. At a high level, the Teradata Analytics Platform is an advanced analytics solution that comes in multi-deployment form factors with the capability of ingesting diverse data types at scale upon which native analytic engines can be invoked to implement multi-genre analytic functions that deliver and operationalize critical business insights. There are six core capabilities that are likely to provide a unique and significant competitive edge to customers of this solution. They are:
Evolution of the Teradata Database to include multi-data type storage capabilities (e.g., time Series, text) and pre-built analytics functions (e.g., sentiment extraction, time series modeling).
Aster Analytics analytic engine with over 180 pre-built advanced analytics functions that span multiple genres such as Graph, Statistics, Text and Sentiment, Machine and Deep Learning, and Data Preparation.
A customer friendly deployment and pricing option set (in-house, hybrid cloud, managed cloud, term and perpetual licensing) that ensures flexibility in accommodating changing customer preferences without impacting any current investments.
A Data Science friendly analytic environment that includes a variety of development tools and languages (e.g., KNIME, R, Dataiku, Python)
An active and healthy integration with open-source analytic engines (e.g., Spark, Tensorflow) and storage solutions (e.g., Hadoop) that aims to provide a customized solution that dovetails with a customer’s current investments and desired ecosystem mix.
A highly performant solution where the insights delivery and operationalization are tightly coupled in the same environment without having to artificially separate them.
Addressing the Analytics Challenges
So, now that you know what the Teradata Analytics Platform is it behooves me to close the loop and discuss briefly how it fixes the challenges that I had outlined earlier. Fortunately, for me, this is an easy and delightful exercise. For one thing, the features above clearly speak to the solution’s capability to ingest and process data of all types (our first challenge). The fact that it comes with a mind-boggling array of analytic capabilities, not to mention the capability to leverage open source analytic engines such as Tensorflow clearly indicates that Data Scientists and other analytics professionals have the ease and flexibility to choose from a colorful palette of techniques to effectively do their work. And what’s more, they can do their work using development tools and languages that they’re most comfortable with (our second challenge). Finally, given that the Teradata Analytics Platform was conceived with a “customer first” mentality – a hallmark of the Teradata way of doing business – it is available for deployment in ways that suit the customer’s unique business needs. Customers who prefer to analyze their data on the public cloud will have the option of buying a subscription to this solution. Alternatively, those that prefer an in house implementation can have their choice fulfilled as well. Customers who choose one deployment option to begin with and decide to change to something else won’t have the worry of a repriced solution as the pricing unit (TCore) is the same across all deployments (our third challenge).
Teradata Analytics Platform, the smart choice
Clearly, and honestly, my conclusion is not likely to culminate in a dramatic denouement. Be that as it may, it is a logical choice to opt for the Teradata Analytics Platform that puts the power of analytics in the hand of the customer and delivers a unique purchasing experience that is quite revolutionary in the market.
Sri Raghavan is a Senior Global Product Marketing Manager at Teradata and is in the big data area with responsibility for the AsterAnalytics solution and all ecosystem partner integrations with Aster. Sri has more than 20 years of experience in advanced analytics and has had various senior data science and analytics roles in Investment Banking, Finance, Healthcare and Pharmaceutical, Government, and Application Performance Management (APM) practices. He has two Master’s degrees in Quantitative Economics and International Relations respectively from Temple University, PA and completed his Doctoral coursework in Business from the University of Wisconsin-Madison. Sri is passionate about reading fiction and playing music and often likes to infuse his professional work with references to classic rock lyrics, AC/DC excluded.