Is a Big Data Strategy really for you?

March 1, 2020 By 0 Comments


Big data has been around for a while — it’s not new; it’s confusing, and it might not be the cookie everyone wants to eat. Is a Big Data Strategy really for you?

In reality, Big Data is still an evolving technology. Some industries have found uses cases for it; others haven’t.

However, it certainly remains the driving force behind many ongoing waves of digital transformation, including artificial intelligence, data science and the Internet of Things (IoT).

Big data and data science, in general, have the potential to reward businesses with huge returns.

Big data isn’t for every business.

Not every business or enterprise that wants to make Big Data work can make it happen unless you have special expertise, budgets,  significant investments in building sophisticated talent, acquiring technology, capable tools, and established processes.  So ask, Is a Big Data Strategy really for you?

If you want to embrace Big Data, you’d first need to determine your Big Data strategy and assess your current analytics maturity before investing in talent or technology.

Before you spend time, cash, resources, an arm, and a leg, it helps to determine if embracing Big Data is really the best thing you can do for your business.

Here are a few pointers that can help:

  1.  Ask yourself the Big Questions About Big Data 

Some industries lend themselves well for Big Data initiatives (apart from what huge businesses like Google, Amazon, and Facebook use Big Data for).

Industries such as traditional financial services, fintech, risk assessment firms, accounting, auditing, healthcare, and fraud detection already have existing frameworks to make Big Data work for them.

In a few other industries such as healthcare, Big Data can even save lives or make lives better.

If your company isn’t a part of any of those industries, you’ll do well to ask yourself some hard questions.

  • How transformative is Big Data for your industry? Do you see any real transformative scenarios based on Big Data or related technologies that your competitors are doing?
  • How receptive is the senior management to Big Data and data-driven decision making? Are they in a position to consider the change?
  • Does your business have rich internal and external data?
  • What kind of data gathering, data management, and data analysis should you consider?
  • What specific and tangible benefits can Big Data Strategy provide for your business?
  • What’s the ROI for your Big Data Implementation?
  • How will Big Data help in achieving business objectives?
  • Considering the answers to the questions above, what would be initial focus areas for using Big Data, as far as your business is concerned?

2.  Big Data Strategy Implementation: Assess Functional Maturity 

Are you ready for Big Data? Maybe you are. Maybe you are not.

In a recent Gartner survey [], 87.5% of respondents had low data and analytics maturity, falling into “basic” or “opportunistic” categories.

Your Big Data readiness comes from an understanding of where you are with respect to business intelligence analytics.

Without digging too deep, many businesses are still at the low-level or basic analytical capabilities largely driven by spread-sheet based analytical models and decision making.

To go for Big Data would mean that you’d need to assess the functional maturity of your business.

According to Garter, here’s how the model looks like:

Big Data Strategy? Is it Real? Hyped? Is it Really For You? Here’s a Ready Reckoner

Big Data Strategy? Is it Real? Hyped? Is it Really For You? Here’s a Ready Reckoner

A guide to analytical descriptions

Simply put, analytical capabilities (or the Standard Operating Procedure) for most companies fall into one of the following:

Descriptive Analysis:

This is the stage most businesses are at. Spread-sheet driven basic data analytics capacities that are either limited by the volume or quality of data, the variation of data points, or simply the lack of vision or will to use data. Detailed sets of reports or data pertaining to what happened in the past. E.g: How many customers made purchases in the last 1,2, or 3 years? Which product category made to the top of the sales chart? What’s the ratio of sales Vs profit like?

Diagnostic Analysis:

If you want to dig a little deeper and get a rather wholesome story of your business, you’d consider diagnostic analysis. Answering questions such as: Why do customers purchase this product variant Vs that product variant? What explains the runaway success of a competitor’s product X — which closely competes for our own product Y? Why is it that our sales are consistently declining in these particular cities or countries?

Several contemporary analytical tools such as Tableau can help analyze these data points and help explore data, test hypotheses, and identify root causes for each point in consideration. Traditionally, several companies only depend on post-purchase data, interviews, and surveys to get information.

Predictive Analysis:

The third-level of analytics is predictive in nature, answering questions such as What’s going to happen to the sales of a product if we add features A, B, and C? If we opened dedicated stores in the following locations (cities or countries), what might be the increase in sales, revenue, and profits?

Note that currently, only 13% of companies use predictive analytics capabilities extensively; whereas, only 3% of companies use prescriptive analytics.

However, 75% of companies are planning to use predictive analytics in the future.

The “Not So Ordinary” Skillsets Needed for Big Data 

If you want to make Big Data work for your business, you’ll need special people with specific and advanced skillsets.

While your usual analytics teams can work with diagnostic and/or descriptive analytics, predictive and prescriptive analytics requires sophisticated Data Science skills, typically not found on traditional analytics teams.

When you are looking to hire, outsource, or use skilled people for Big data, you are looking at skills such as data visualization, general-purpose programming, data mining, or machine learning.

Or, you’d need software-specific skills (in addition to the above) such as Apache Hadoop, Apache Spark, MatLab, and others.

In consideration of the above, are you really ready for Big Data?

If you need help with expert consultation, an audit of your existing analytical capabilities, or an assessment of what’s best for your business, do get in touch with us for a consultation [].