Think “Smart” Data Analytics before you think “Big” Data Analytics…

Big Data is typically explained via 3Vs – Volume (2.5 Quintillion Bytes of data are estimated to be created every day), Variety (data from all possible source from structured to unstructured) and Velocity (tremendous speed of generating data due to increasing digitization of society).

Organizations using Big Data tend to have a better understanding of their customers, products, operations and competitors to drive innovation (new products and services), operational efficiencies, customer delight, increased revenue and low costs.

However, unlike what all the Big Data Proponents who might want you to believe-you should NOT be obsessed with volume, or variety or velocity of data- it is rather important to focus on the,  the VALUE that you can derive from the data acquired. It can help you  make the best business decisions, draw the right information at the right time.

‘Smart’ Data Analytics involves bringing together the power of data visualization and predictive analytics to derive actionable data-driven insights which can leverage the existing data/information captured by the business.

So, how is “Smart” Data Analytics different from Big-Data Analytics?

This is Step 1.0 of your Data Analytics Journey before you go ahead and make the Big-Data investments.

For beginners , this approach recommends mining through and deriving actionable insights from the data that you already have in your organization across the ERP, Point of sale or multiple other systems.

Don’t get me wrong – as you move up the analytics maturity curve:

  • You will need to invest in an Enterprise-wide Data warehouse and address any data quality issues – but, “poor data quality” is not a show stopper for deriving data based insights for your business today!
  • You can potentially go ahead and invest in capturing more data about your customers across social media platforms etc. to get better insights – but this is beneficial only after you’ve already made best use of the information you already have about your customers.

Another difference would be, Big Data Analytics Platforms will help you mine through the data faster via parallel processing – however  if you’re not making use of that information in real-time it will not create any incremental benefit.

For example: As an insurance company, if you mine through all customer data to determine the optimal premium to be quoted for the insurance policy for customer X, and then send out an email or mailer to this effect to him, it doesn’t matter whether you arrived at this answer in micro-seconds or couple of seconds. It will however make a difference if you wanted to share this optimal price information with the customer in real-time while he was filling up your application form online. It works too…

How exactly will “Smart” Data Analytics help you?

  • It will help you move from a retroactive and intuitive decision-making process to a proactive data-driven one.
  • It will help you become an information-led organization and sharpen your competitive edge.

Thus, helping you to deliver benefits including : attracting more valuable and loyal customers, charging prices closer to the market rate, ensuring more focused and relevant marketing campaigns, running more-efficient and less-risky supply chains, ensuring the best product or service quality levels, ensuring highly individualized customer service and guaranteeing a deep understanding of how process performance drives financial performance.

How can you bring in “Smart” Data Analytics to your organization?

  • Define key business areas where you’d like to improve decision-making.
  • Work with an analytics partner for 2-3 Pilot engagements across the identified business areas to experience first-hand the “value” that can be delivered for your business via analytics.
  • Finalize your analytics strategy (in-house capability development or working with analytics partners or mix of in-house and analytics partners) basis the “value” delivered in the Pilot engagement.
  • Prioritize and roll-out analytics solutions across business processes basis ‘potential value’. Solutions include visualization dashboards to provide insights into business operations, predictive models to optimize decision making and prescriptive analytics solutions to reduce time to respond to customer actions and standardize best practices across the organization.
  • Ensure executive sponsorship of the initiative to drive analytics adoption across the organization.
  • Continuous monitoring of ROI from analytics will help streamline the analytics strategy.

 What’s the way forward?

It is expected that most CXOs should do a balancing act between investments for future (Big Data Analytics) and deriving value from data today (invest in ‘Smart’ Data Analytics). However, as they progress through the year, one additional item that is expected to move up on their priority list will be creating an analytics culture in the organization. To derive the maximum insights from data and leverage it to optimize decision making across strategic — operational — tactical level would require focused change management efforts along with strong and sustained top-down sponsorship. This will become the key for maximizing the ROI from ‘Smart’ data analytics, and will help build the right business case for future investments by organizations in Big Data tools/technologies.

3 thoughts on “Think “Smart” Data Analytics before you think “Big” Data Analytics…

  1. Pingback: E-commerce and cyber crime-What’s the way out? | Advisory India Blog

  2. Hi, Logical write up . A few points adding on to what you are pointing to.

    Firstly, The strategy an organization should take (considering it is a big one with Enterprise Information management and BI tools already in place) is to engage experts (from within or external) who can identify which set of platform and tools are the most appropriate ones for the organization based on the current scenario. What I am seeing around is , this is where most of the companies are going wrong. They should realize that Big Data adoption doesn’t mean doing away with existing set of databases, tools, and most importantly analysts(workforce). This workforce has a specific skillset (like SQL, Data visualization tools like Tableau, Marketing automation like Unica/Aprimo, reporting like Cognos/Microstrategy etc). So when a Big Data platform is introduced, it should be designed in such a way that the data components can be stitched with the data present in the EDW for effective use across the existing workforce and not depend on a few “Data Scientists” who are overpaid.

    Secondly, they companies will have to look at long term strategies of making Big Data effective with used case driven approach across various teams of the analytics department.That will help them groom the leadership as well as the workforce to embrace newer trends in technology like Internet of things or else with commendable ease. It is a matter of imbibing the culture of making the workforce understand that technology will play the most important role in the business and theory based knowledge of analytics will not work in the long run. Historically it is seen that the analytics workforce is technically not very sound rather they keep away from technology. But going forward , those who embrace technology will survive the analytics industry.

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