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.