The Hidden Gems of Data Analytics
Much like the rallying cry of the prospectors of the 1840s of “there’s gold in them thar hills,” many businesses are seeking to mine their data lakes to find value in the minutia that makes up structured and unstructured data. However, realizing the value of Big Data takes a lot more than a mule, a pickax, a shovel, and a dash of luck.
Merely deploying the appropriate tools isn’t enough. Today’s modern data treasure hunts require that data scientists and their staffs know what treasure to look for. In most cases, technology professionals just focus on the obvious — such as customer engagement, sales goals, and so forth to justify their mining efforts — potentially ignoring the hidden gems of insight that data analytics can deliver.
The biggest challenge may come in the form of the data itself: For proper analytics, you must have good data. That said, technologies are evolving that help to gather data and format it in such a way that it’s more usable. Technologies such as AI, machine learning, and even big data analytics platforms go a long way towards normalizing data, which helps to eliminate the tendency of “playing it by ear,” where gut instincts of what the data will deliver could potentially lead to disaster.
Types of data
Data is normally broken down into two distinct forms: unstructured data and structured data. In its simplest form, unstructured data refers to data that does not adhere to a predefined data model and is not organized in any particular way. For the most part, unstructured data consists of text or other elements that are not indexed or defined. Unstructured data can contain important data elements, such as dates, numbers, links, and so on.
Structured data is data that is stored in a highly organized fashion, using indexes, tables, columns, schemas, and so forth. Most structured data takes the form of a database (either relational or query based), making it easier to identify and mine critical data elements.
Realizing the value of data
Knowing what to ask of the data is the most important step of a data analytics process. Asking those questions means having an understanding of business processes, as well as sense of the value that can be derived. For most, that is an obvious step, consisting of simple questions such as “what were the regional sales of last quarter” or “what were the busiest times for tech support.” The possibilities are almost limitless when focusing on traditional business operations questions.
However, those standardized questions and queries are only a small part of what data analytics can offer. The trick here is to work with the data in such a way to develop insights, ones that can deliver on the “what ifs” of business processes.
For example, a business may want to judge the impact that a particular change might have. Case in point would be a customer services department looking to expand their hours and better serve customers. That analysis would require developing an algorithm that looks at missed calls (outside the scope of normal hours), calls that exceeded the normal termination of business hours, as well as emails and other content related to support.
It’s those what-if scenarios that can deliver the biggest value to businesses. Instead of focusing on how everything was done in the past, data scientist need to develop the concepts of cause and effect to derive the most value out of data.
The path forward
Improving the value offered by data means understanding what problems are facing the business. Companies are starting to ask questions about customers, products, and partners in new ways. For example, is the business looking to better manage customer interactions using in-depth and customized information about that customer?
As part of the drive towards continuous improvement, companies are beginning to focus on real-time analytics that deliver scenarios such as “How can I bring happiness to this customer and anticipate their specific needs?”
Solutions such as that require defining and analyzing data sources. In other words, analysts have to code algorithms to deliver the information needed to make the right offer to a customer deciding on a purchase. Furthermore, data analysts may seek to glean data from outside sources such as social media data. Or determine what big data sources are available internally that were previously underutilized. One example is the use of text analytics to garner insight about customers from call center notes, emails, and voicemails.
For many organizations, the key goal with big data analytics may be to look for patterns and relationships that apply to the business and then filter that data based upon a particular business context. In that way, big data analytics will help you find those small treasures of information in your big data.
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