Making Big Data Work for Your Business: A clear, practical and simple guide to ensuring effective Big Data analytics for your business

Often, organizational strategies evolve around entrenched perceptions; trends identified through sampling and organizational aspirations. Past performances and future prospects play a key role in this process. Organizations consider macro-economic environments, market trends, industry growth, competitive actions, core capabilities and adjacencies, and finally shareholder expectations in building their business strategy. In the past, such an approach has been able to keep pace with the evolution of various forces that have an impact on an organization's performance. Today, the rate of innovation, interventions, and insights around their current and future impact outpaces many other factors that impact the long-term or even medium-term sustainability of an organization's growth and profitability. Businesses need agility to respond to such challenges. Big companies sometimes get pulled back by their size and momentum; smaller companies sometimes lack the resources and reach to invest in response.

Building strategies around big data analytics is about finding value in data, and it is about enabling value through data. The following are seven simple steps that will help you do this:

Throughout this chapter, I'll take you through each stage to ensure that your big data strategy is closely tied to your organization's strategy, which in turn will ensure that you are able to get the most from your data.

However, before we proceed further, it's a good idea to compile a list of various sources of revenue or various areas of key operations for your organization in a business catalog. For each of the revenue sources or operation areas, briefly capture how the company makes money from them and what the fundamental components of their operation are. This acts as a good compass for you to put some of the upcoming discussions in this and future chapters into context. It could be in a very simple manner as follows:

The first step towards interlacing big data analytics into your organization's strategic framework is to understand how big data is changing the backdrop in which your company operates. This requires a shift in mindset. We are attuned to think of a changing landscape in terms of actions and outcomes. We have been trained that knowledge is power; we have been taught that history never repeats itself. However, these beliefs are no longer considered as absolute truths. Traditional strategic analysis techniques relied heavily on causality. Our core competencies revolved around what we can do, what we are good at, and what our reach is. Capabilities around what we know, what more we can find out, how we can connect the dots around such information, and how we can use these insights to change the business have not been generally considered core competencies. That is, until the advent of big data.

Imagine you have a service that helps travelers book hotel rooms and rent cars. You will have systems and capabilities to understand inventory movement in hotels in your network, possibly in other networks as well. You will also have ways to auction new bids from your travelers, and it is also likely that you will be able to adapt according to specific events, seasonality, and past data to arrive at price recommendations for both hotels as well as travelers.

Now, imagine if someone were able to modulate your hotel room pricing strategy with changes in plane ticket booking data. This could change the dynamics completely and give that person more opportunities for early profitable pricing and increased customer traffic.

In another example, consider yourself as the manufacturer of household or light commercial electronics goods, say, an industrial grade water purifier. You are probably using lot of data based on:

You are also likely to have a lot of data around parts replacement and warranties, which you probably use for part stocking and pricing types of decisions. Imagine if somebody could simulate your parts' usage data, maintenance of data on your equipment, and how your customers actually use your equipment. Precisely how you make money through maintenance programs and part sales is captured in this data. This data is not proprietary to you; your customers or their maintenance service contractors most likely own the data and somebody could just get it from them or buy it. They can now come up with very innovative maintenance programs for your equipment and take a substantial portion of your business away; you might be reduced to only a provider of proprietary parts.

As an illustration of a practical scenario for the preceding example, in large urban cities of emerging markets where potable water is a big issue, your company has decided to offer the base water purifier unit to households at a lesser margin, thereby trying to make the entry point attractive for customers. As a strategy, you recover your lost margin through replacements of purification candles, which frequently go bad. There is most likely nothing very proprietary or unique about the candle and changing those requires only basic technical skills. So, any small local entrepreneur can also take away your business, thus challenging your profits.

In both cases, you could be that "someone" if you understand the changing landscape stimulated by the power of big data.

To understand the changing landscape in the context of big data, there are six questions you need to consider:

By answering these questions, you should go beyond data that is easily captured; in fact, you should not even consider whether you capture these data elements today. Your compass in this exercise should be whether somebody or a system in your organization knows about these various data elements in any form—structured, unstructured, or streaming. These questions and the others that follow in this chapter might seem a bit tactical and low level compared to normal high-level strategic considerations, but because Big Data Analytics is so new, to be successful it is critical to build a solid foundation of understanding, with a lot of detail at a lower level to enable you to build the strategic framework. You need to avoid simply extrapolating general concepts and then basking in the luxury of making broad assumptions.

Once you have answered these questions, you need to compile the long list into an information catalog. Do not get distracted by the desire to classify the information elements into logical clusters at this point. Simply make the list and move on to the next step. In the following discussion, whenever we refer to information or data elements, we will consider all of what is available and what's possible, irrespective of ownership, collection, or storage.

To understand strategic implications, you first need to understand the current role of data in your business. In order to help your understanding, you need to explore answers to four key questions:

Answers to these questions will lead you to areas that may be blind spots for your organization today in terms of both opportunities and threats. For example, in the previous example of the water purifier manufacturer, if you do not use maintenance records as a consideration in your revenue strategy, the implication is that you are possibly not exploiting a new maintenance model or replacement part sales opportunities, or even that somebody else might start doing it.

Let us consider another example. Anita has been banking with one particular bank for over 16 years and is extremely satisfied with their services. For all these years, she held salaried jobs and (thankfully) her salary grew several times. In addition to savings accounts, she has engaged in other types of transactions with this bank—all experiences were very satisfactory. However, recently she realized that this bank has handled less than 20 percent of her money. The bank did depute competent and nice managers to attract her business, and provided impeccable customer service to build credibility. They missed out on tracking her over the past 16 years across their various divisions to meet her needs of the hour. They had all her data, but used a quarterly savings account balance to pursue opportunities with her. This is another example of failing to understand strategic implications of data and analytics.

The problem is not the availability of data; it is appreciation of the value of data and what it can do for the business. Comprehending the strategic implications will lead you to conclusions that are not explicitly stated or obvious at a casual glance.