Why Big Data Matters

Knowing what Big Data is and knowing its value are two different things. Even with an understanding of Big Data analytics, the value of the information can still be difficult to visualize. At first glance, the well of structured, unstructured, and semistructured data seems almost unfathomable, with each bucket drawn being little more than a mishmash of unrelated data elements.
Finding what matters and why it matters is one of the first steps in drinking from the well of Big Data and the key to avoid drowning in information. However, this question still remains: Why does Big Data matter? It seems difficult to answer for small and medium businesses, especially those that have shunned business intelligence solutions in the past and have come to rely on other methods to develop their markets and meet their goals.
For the enterprise market, Big Data analytics has proven its value, and examples abound. Companies such as Facebook, Amazon, and Google have come to rely on Big Data analytics as part of their primary marketing schemes as well as a means of servicing their customers better.
For example, Amazon has leveraged its Big Data well to create an extremely accurate representation of what products a customer should buy. Amazon accomplishes that by storing each customer’s searches and purchases and almost any other piece of information available, and then applying algorithms to that information to compare one customer’s information with all of the other customers’ information.
Amazon has learned the key trick of extracting value from a large data well and has applied performance and depth to a massive amount of data to determine what is important and what is extraneous. The company has successfully captured the data “exhaust” that any customer or potential customer has left behind to build an innovative recommendation and marketing data element.
The results are real and measurable, and they offer a practical advantage for a customer. Take, for example, a customer buying a jacket in a snowy region. Why not suggest purchasing gloves to match, or boots, as well as a snow shovel, an ice melt, and tire chains? For an in-store salesperson, those recommendations may come naturally; for Amazon, Big Data analytics is able to interpret trends and bring understanding to the purchasing process by simply looking at what customers are buying, where they are buying it, and what they have purchased in the past. Those data, combined with other public data such as census, meteorological, and even social networking data, create a unique capability that services the customer and Amazon as well.
Much the same can be said for Facebook, where Big Data comes into play for critical features such as friend suggestions, targeted ads, and other member-focused offerings. Facebook is able to accumulate information by using analytics that leverage pattern recognition, data mash-ups, and several other data sources, such as a user’s preferences, history, and current activity. Those data are mined, along with the data from all of the other users, to create focused recommendations, which are reported to be quite accurate for the majority of users

Intended audience

I anticipate that there will be three types of individuals listening to that debate, each hoping to hear something different. First, there will be DWBI professionals who are  currently leading a development team, following traditional methods. Well over half of team leads already feel that their developers are moving far too slow for their projects to succeed. These readers will be looking for a way to accelerate their programming  in the next couple of days. This book provides a quick way to get started with agile methods and then a step-by-step path to enhance the method as each team matures with the practice.

The second group within the audience will probably be DWBI directors. They will be curious as to whether enterprise-level analytics can be implemented using the iterative and incremental approach that many of the agile methods champion.