What Is Data Analytics and Why Should You Care?

Let’s tackle the second question first, because we think it’s more interesting: why should you care about data analytics?

Today’s data streams in from more sources than ever. Gadgets like Amazon Alexa and Google Home learn your behavior in order to anticipate your needs and wants so they can serve a better experience. When you pull out your smartphone in the aisle at a brick-and-mortar to use the store app, that app will use location services, see that you’re in the store, and serve you an ad or offer to encourage you to buy--usually something you’ve bought or looked at before.  

While we take these experiences for granted there is a lot going on in the background to make it happen--and it all relies on big data analytics. Businesses like Amazon and Google, who are on the forefront of data analytics and processing, have seen extraordinary success by using their data as an asset. Those who are not at the leading edge but manage to keep up often go through an enterprise-wide refresh to modernize the brand’s identity and goals in light of today’s digital economy (a digital transformation strategy). Those still struggling to find value in their data quickly fall behind and off the radar completely.

In other words, we care about data and analytics because they are fundamental to doing business in today’s world. People, processes and technology alone just don’t cut it anymore. Data is the fourth pillar, and it is absolutely mandatory for success.

Now the first question: what exactly does data analytics encompass? What are the key terms you need to know to have an intelligent conversation on this topic?

Here are some definitions of the three most popular ways of describing data analytics:

1. Data Science: refers to anything involving the gathering, storage and usage of data. This is an interdisciplinary field using scientific methods, processes, algorithms and systems to extract knowledge and insights from data in both structured and unstructured forms. An example of unstructured data would be a conversation string on Facebook about your brand, or comments recorded from an administrator during a medical study.
2. Data Analytics: refers to the process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information and supporting decision-making. In other words, it’s about applying data science methods to big data.
3. Big Data: refers to very large data sets that must be analyzed computationally to reveal patterns, trends and associations. A related term you may hear is “data lake,” which refers to a large compendium of data that is likely to be raw and disorganized. Similarly, a data warehouse is a system that can be built to organize that data and make it available enterprise-wide to help guide decision-making.

Gartner recently discovered that data literacy was the third biggest hurdle preventing CDOs and their teams from accomplishing their goals. For an organization to use data strategically to solve clearly defined business problems and achieve specific goals (rather than as part of a vague and lofty goal to “make better use of our data”) a focus on building data literacy is pivotal. However, most business users can’t expect to become so fluent in data analytics that they can single-handedly solve an organization’s data gaps.

Every organization needs the help of data scientists that possess a thorough understanding of the organization’s industry as well as the ability to apply agile business principles to their work. After all, data analytics is all about building better business intelligence, not just using data for using data’s sake.

Most organizations need help setting up their data strategy from the top down in a way that focuses on business needs first. When Marlabs engages with a client, we work closely with them to build data literacy as an organizational competency. We’re happy to do the legwork as long as our clients need it, but the greater goal is to embed the right skills within the organization.

What are the first steps for an organization to build a relevant and successful data strategy?

1. First, get the right people involved. Find anyone who may already be an advocate for turning your data into an asset, and get them on your team. Make it a grassroots movement if you need to.
2. Second, identify the business challenges or opportunities you’d like to solve or achieve using data analytics. Start with what the business needs, not with what you think the data could do.
3. Third, identify current capabilities so you can determine the right technologies and talent needed to move forward. In other words, look for the gaps. Marlabs is an excellent resource for helping you with this step (or any other) so don’t hesitate to contact us to jumpstart your efforts.
   

Now that you’ve got the inside scoop on what data analytics really is and why it’s so popular, you probably want to know more. Stay tuned to this blog for more Data Analytics 101 content, including overviews of the various types of data analytics you should know about.