Big Data has become a key buzzword in today’s business landscape. But it’s truly more than that; it is a movement. It has the remarkable ability to completely transform a business if used effectively. The application of Big Data can often seem complex and overwhelming to many companies, especially to various SMBs, but it doesn’t have to be. It is becoming more crucial that companies of all sizes recognize and utilize Big Data in an increasingly competitive marketplace. Following a simple one-two approach can be the solution to adopt a data-driven strategy in the workplace.
The term has as many definitions as it does applications. For discussion here it refers to exceptionally large data sets, both internal and external, that can be analyzed to reveal trends, patterns, and insights to grow an organization. There is a straight forward formula for making the utilization of Big Data manageable for companies of various sizes. First, analyze your internal data and second, utilize relevant external data. Keep it simple.
Internal Data
Internal data is information that comes directly from an organization’s systems, such as that related to operations and transactions. For example, a company’s CRM can provide extremely valuable data that can be analyzed, depending on the data which was input. Generally, the types of internal data may include: Sales Data (ie., revenue, price points, closing cycles, customer surveys, distribution channels); Financial Data (ie., expenses, cash flow reports, etc); and HR Data (ie., employee satisfaction rates, onboarding metrics, exit interviews etc).
Tracking and analyzing a company’s data can be as sophisticated as hiring a team of data scientists to as uncomplicated as purchasing data analytics software that will provide you the tools necessary. A quick Google search can provide a plethora of software options, depending on an organization’s needs.
Internal Data Use Case
Perhaps one of the most notable examples of a company effectively using its internal data is by no surprise, Amazon. The online retail juggernaut gathers information on every single customer, including: what customers buy, what customers look at, shipping address, items in carts and wish lists, and feedback provided. The company uses this data to find other customers that fit into the exact same customer niche and make recommendations based on what those others purchase. Utilization of this method generates an incredible 35% of the company’s revenue.
External Data
External data is defined as any data that is generated outside of an organization. This data could take the form of public data or third-party organizations. Financial trends, customer demographics, online search queries, and more can assist a company find the best ways to grow and reach its target market.
Public data is easy to discover and if companies aren’t using the resources freely available to them, they could be passing revenue by. Examples of public data that can be obtained are Government Data (ie., data.gov, voter data, licensing boards), Social Data (ie., social media sites), Economic Data (ie., World Bank Open Data, Federal Reserve Economic Database), Health Data (ie., healthdata.gov, FDA). Other sources that can provide great insights are Google Alerts, Google Trends, Microsoft Research Open Data, Google Dataset Search just to name a few. The amount of public data is massive. If a company doesn’t have the time or resources to search themselves, there are data marketplaces that aggregate data and sell for a fee or offer a subscription service. There are nearly endless amounts of data providers out there for virtually any type of data.
External Data Use Case
A very straightforward example of an industry that benefits from weather data is grocery retail. People change their buying habits based on weather. For example, prior to a predicted storm, people will buy in bulk and stock up on essentials such as bread and milk. Therefore, grocery stores will take advantage of weather-based advertising.
There are many ways public data can be used that may not be obvious. For example, a company could use voter data to market its product to a certain political demographic. Another example could be a company selling a certain insurance product using licensure data. In some instances, competitor lists can even be obtained by deep searching. The amount of data available is quite astonishing if you know where or how to find it.
Summarizing the One-Two Big Data Approach
To recap, an organization should first analyze its internal data to determine trends and insights. This information alone can be very powerful. However, the knowledge obtained through this analysis will assist in determining what type of external data would be valuable. Analyzing internal data can provide a much better understanding of what external data to unearth. Both types of data should be used in conjunction with each other for a formidable one-two punch! Following this formula will provide a powerful data-driven strategy for any organization.
By Tammy Onedera, CEO, Seraphim Analytics
Tammy has been an entrepreneur for the last 20 years, co-founding four companies, two of which in the tech industry. She currently serves as the CEO of Seraphim Analytics, a boutique niche-data company that focuses on competitive intelligence. Utilizing her sales background, marketing and research skills, Seraphim Analytics has assisted in five mergers and two acquisitions, totaling over $2 billion. Having received her BS in Clinical/Community Psychology from the University of Michigan, she brings a unique perspective of human understanding in relation to the business landscape, combined with her MBA. Tammy’s 10+ years in complex, consultative sales has led to great success utilizing relationship selling with a solution-based, customer-centric focus and assists relevant companies in this approach that employ Seraphim Analytics data. In addition, Tammy is an investor in early-stage start-ups and is passionate about helping small businesses have the tools to grow that often only large enterprises can afford. Because of this, she developed Seraphim Analytics model of “Data for Equity,” which still thrives today.