Understanding the changing landscape
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:
Customer segmentation: How your customers are categorized into various demographic groups with different buying patterns
Pricing strategies: How you sell your products at different price points for different markets or different sales channels
Component or finished product sourcing: Where you buy things that become part of your products
Inventory levels: What kind of stock you hold at different stages and locations of your manufacturing or distribution processes
Quality improvements: What new features you have added to your product to address customer needs or expectations that have not been met
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:
Do you know all the data you have?
Do you understand all the data you have?
Who else has similar data?
What data are you using and how?
How are others using similar data?
What data from other sources do you use in your business?
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.
