How to Make Your Company’s Data Actionable

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Big data is a big deal for today’s companies. The concept of using data to drive sales and improve customer satisfaction is so alluring to businesses that many organizations have begun investing blindly in big data platforms and analytics tools. But simply throwing money at platforms and tools won’t solve most problems businesses are trying to solve and only a few organizations are truly reaping the benefits big data offers.

Traditional data collection and analysis tools may not be effective when leveraging big data empowering companies trying to maintain their competitive edge. The sheer volume of data produced by companies so massive for simple analytics solutions that significant insights are often overlooked. As organizations jostle for the top spot in their respective industries, employing the right tools to ensure they’re maximizing returns and insights from their big data will be critical for businesses looking to reduce costs and increase efficiencies.

Big data empowers companies to make smarter and faster business decisions

For businesses looking to adopt a data-driven mindset, defining a clear set of corporate goals and objectives can help analysts focus on what data they should be mining and what data can be eliminated. Insights derived from focusing on the right data can help optimize sales funnels, predict business outcomes and even transform the way teams operate within an organization.

With the right set of tools and strategy, big data isn’t just a method to benefit the bottom-line — it’s increasingly being leveraged to make real-time business decisions. It can allow doctors to provide lifesaving diagnoses and treatment options to patients as soon as they step into a hospital. Retailers employ high-performance analytics to better understand their target their customer’s demographic and identify ways to improve customer service in-stores. Machine learning and artificial intelligence tools can rapidly capture, filter and analyze data to expedite response times and determine what information is most relevant during the analysis phase. Across industries, companies are leveraging massive amounts of structured and unstructured data to facilitate making complex business decisions.

But collecting just any type of data isn’t enough for businesses to extrapolate useful information. Different objectives rely on different types of data, and identifying the end goal first helps businesses determine what information they should be collecting. Is a company focused on improving customer satisfaction or trying to promote sales of a specific product? Data compiled from social media feeds, for example, might facilitate customer growth strategies but might not be as useful for informing operational excellence. Once businesses commit to investing in big data technology, they need to understand and identify where big data fits in their existing operations and be ready to adjust data collection based on needs and identified business outcomes.

One step at a time: How companies are turning raw data into valuable insights

Some of the biggest challenges for businesses considering big data analytics are the large quantities of data available and underestimating the total scope of work. Basic analytics tools may not be outfitted to accommodate mass quantities of data or deliver insights in real-time, and a lack of support for complex systems can slow down data adoption. By focusing on four key strategies, businesses can take to turn their raw data into actionable insights that drive bottom-line growth.

  • Accelerate decision making with clean data. Data collection can be messy; information is compiled from multiple outlets and not all of the data collected is normalized consistent before analysis. Cleaning or correcting “dirty” data helps analysts remove any information that could lead to misinformed decision making and the normalization of data across platforms makes it easier for teams to understand exactly what they are looking at. Businesses should also consider employing tools equipped with adaptive decision management systems that capture, filter and analyze data as it’s collected in real-time.
  • Don’t underestimate the scope of work. The numerous benefits of big data are so appealing that most companies jump right in without taking into consideration the exact scope of work. Not having the appropriate tools, for example, can hinder progress and even break during analyses if existing solutions aren’t equipped to handle mass amounts of data. Other concerns, like not asking the right questions or failing to analyze the right type of data, also frustrate businesses who might not be seeing the results they want out of their big data.
  • Employ automation to simplify data analysis. Once data is cleaned and internal processes are developed to support high-performance analytics, businesses can turn to automation to increase efficiencies. Automation aids businesses during the data collection process, allowing data scientists to focus more on the analysis part of big data and less on the manual, redundant work. In addition to easing the workflows for data scientists, automation can help businesses reach smarter conclusions faster because analysts are less concerned with the backend.
  • Visualize data to reduce complexities. After data collection comes the analysis, but interpreting large sets of data can be confusing and complex. With data visualization, analytics are presented based on their context and what they are trying to communicate so that decision makers better understand the data. Using a graphical representation of business information makes data easier to digest and allows analysts to make macro level decisions from that information. It also helps data scientists spot data trends quicker and simplifies complex data so other teams can uncover insights specific to their practice areas.

The push to become a data-driven company has businesses rethinking the way they can derive valuable insights from big data for their immediate and future decisions. With the proper plan in place, businesses can take advantage of the opportunities provided by big data analytics and distinguish themselves from their competitors.

About the Author

Ray Johnson is a director and data scientist at SPR. With more than 20 years of experience in information systems, Ray is proficient in developing and delivering solutions and strategies in the areas of big data, IoT, business intelligence, data warehousing and predictive analytics. He recognizes opportunities to apply data science methodologies and predictive analytics to help organizations in achieving and realizing business outcomes.


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  1. Don’t underestimate the scope of work is a big one. People always seem to have unrealistic expectations especially the further away they are from being one of the people to actually DO the work. Communication is critical.

    Also, clean data! I just read another article on the Verge where it said that 80% of a data scientist job was just cleaning data! Using data cleaning software like ours over at Data Ladder can help with that though. Don’t make your tech team clean it the hard way.