Analytics 3.0 and Data-Driven Transformation

by   |   March 7, 2016 5:30 am   |   2 Comments

The advent of mobile, the Internet of Things, and cloud has reinforced the need for a new era of analytics to solve challenges in the customer, product, operations, and marketing domains. And new niche startups armed with data and digital weapons are all set to shake up the market with a wave of digital disruptions. The established companies need to restructure their business and technology landscape. Such transformation helps them to increase their sales and is a matter of survival in the current market conditions, where “data and digital” is the new word of mouth.

A complete data-driven transformation is nearly impossible unless the organization is prepared to deconstruct old-school methodologies and pave the way for establishing a data culture. Like business transformation, data-driven transformation must provide an end-to-end solution. Data-driven transformation calls for a Big Data and Analytics Center of Excellence to be deep-rooted within the organization. Organizations need to involve cross-functional teams like BI, Marketing, IT, and external consultants to establish data governance and set up a data and decision-science culture within the organization. This is especially important for companies that have IT and marketing services departments heavily outsourced because stringent service-level agreements with vendors often kill innovation and don’t provide the space to take risks and “fail fast.”

Forging relationships with a competent, agile, and trusted partner can significantly accelerate your company’s progress. Data governance is your framework for making decisions and a tool to tailor your company’s corporate data rules, so third-party controlled data governance can be challenging and costly.

Treat your Data as your Company’s Most Valuable Asset

To be most effective and efficient, methodologies for combining business and technology transformations require both initiatives running in parallel and complementing each other.

Gartner Research statistics show that data-driven transformation can be initiated beyond the IT domain. The 2015 percentage increase in business unit heads driving initiatives that formerly were championed by a small group of enthusiasts indicates that companies are recognizing the value of big data. Big data is no longer just another IT project. Companies seeking to gain a foothold as a market leader benefit when everyone gets involved.

Source: Gartner research. Click to enlarge.

Source: Gartner research. Click to enlarge.

 

Why Companies Have Struggled with Traditional Analytics

Analytics has evolved in three stages: Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. We are in the era of Analytics 3.0, when tools make decisions and measure the impact. A true data-driven transformation requires moving to Analytics 3.0.

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Prior eras utilized traditional description analytics and data warehousing. The problem with this approach is that descriptive analytics is the postmortem analysis: It tells what happened before. In Analytics 3.0, prescriptive analytics (what should I do?) minimizes human input and eliminates descriptive analytics (what happened?), using technology to facilitate decision support and automation. This creates disruption and allows companies to catapult ahead of the competition.

Big data, as we know it today, has begun to hit a plateau in productivity. However, merged with Analytics 3.0, I expect that the most exciting success stories will emerge in 2016 and the next several years to follow.

RFM: Recency, Frequency, and Monetary

The traditional RFM model is popular in marketing for analyzing customer value. Prior to big data and Analytics 3.0, manual, time-consuming interventions were required. Decisions were based on printed reports and creating dashboards. Using big data and analytics, traditional customer-segmentation reports are eliminated, and the system solution predicts future customer behaviors with additional classification rules such as geo-location, age, and gender. The new RFM model (shown on the right below) is supported by massive computation power, machine learning, and complex rules engines. Together, the future buying behavior of customers can be predicted, helping companies retain customers with suggested product and/or services recommendations.

Click to enlarge.

Click to enlarge.

 

The New Marketing Funnel

Another challenge is fueling the modern marketing funnel, which needs a continuous data exchange between the data warehouse and cloud-driven marketing automation, often called the marketing cloud. Combining data from social networks, mobile apps, CRM, clickstream data, and the marketing cloud requires massive bulldozing of structured and unstructured data. Additionally, different sources of data must be massaged, filtered, and merged. Traditional data warehouses cannot handle complex workloads that vary in all dimensions (volume, velocity, and variety).

Click to enlarge.

Click to enlarge.

 

With terabytes of data in ever-growing volumes, one can easily visualize the need for big change. Big change starts with a small step, and your company can start small by simply gathering your business questions and intuitions about your industry’s future.

Companies undergoing big data driven business and technology transformation are positioning themselves as disruptive industry leaders. Look at any industry within the past decade and you will find a disruptive startup that used big data to gain competitive advantage. Uber, for example, a car company that doesn’t own vehicles, has been the “uber” of disruptions. Other examples include Pandora and Netflix.

Historically, business and technology decisions were made with some measure of gut instinct, much like descriptive analytics (analyzing the past). Today’s business and technology transformation requires intuition, looking to the future. Unlike gut instincts, however, intuition can take a prescriptive approach. No longer will companies have to “wait and see” what happens to gauge success or failure. With Big Data and Analytics 3.0 transformation, the measurement of success is real time.

Chandramohan Kannusamy is an intuitive technologist with strong business acumen, delivering practical, results-oriented expertise in the big data space. He is driven by innovation and entrepreneurial spirit in big data strategy, technical consulting, and proof of concepts, implementation, and architecture.


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2 Comments

  1. Dejan
    Posted March 8, 2016 at 9:52 am | Permalink

    Great and very informative post.

  2. Sateesh
    Posted March 17, 2016 at 5:50 am | Permalink

    Good Article but for me it looks like people are trying to combine multiple concepts without actually understanding the business needs and various analytical models/algorithms. For example RFM modeling, The purpose of of RFM modeling to understand customer value. Even though there are multiple ways to identify customer value, RFM became popular because of data requirements and simplicity. Most of the time, a retailer won’t know social profile, location of a customer, sometimes not even age. When it comes to RFM models, the focus is mainly on transnational data, which is readily available. Business is using propensity models (future buying behavior) to understand consumer behavior when they have behavioral data and it is nothing new. But when you write RFM as a behavior prediction model, then the essence of RFM model is lost. That is not what people are trying to do in Analytics 3.0.

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