5 ways to turn big data into insights

No matter how vast your big data sources are, if your company does not devise the right methods for garnering meaningful insights from it, the data is of no avail.

The entry of big data has revolutionized the way businesses work. However, till today, a large number of decision makers are confused on how to extract the right insights from big data. This is mainly because businesses embark on this journey without checking if they have all the parameters in place. Majority of big data projects are implemented after insight expiry or with defective strategies. Before tackling a voluminous amount of data, it is crucial that businesses formulate an apt big data initiative to suit their needs. In our experience, these 5 ways are common to successful businesses and are an effective guide for turning big data into big insights.

Determine what’s actionable

Before you start extracting insights from big data, you need to have a clear understanding of the things you want to achieve from it. Distinguish between the strong areas of your business and those that need re-consideration. Before diving in for answers,it is important to have  the right set of questions for big data and its analytics. Address those questions first that you know are bound to lead to economic opportunities and are practically actionable. It is easy to get distracted by the vast availability of big data and its exploration. Thus, narrow your approach to core business problems. Set achievable parameters, otherwise, you will risk the wastage of manpower and valuable resources.

Assemble a ‘smart’ team

The second step is to assemble a team of skilled professionals. Actionable insights can only be garnered from big data effectively with the assistance of intelligent humans. There needs to be a presence of creative personnel who can formulate new ideas, develop technological strategies, and effectuate efficient implementation. Look for individuals who have fair knowledge in the fields of AI, machine learning, big data and its analytics, automated support systems, and the like. Look for team players; people who are adaptable and receptive to the continuous change in data and technology. Big data is useful, but without any humans in the loop, it may end up creating more problems than it solves.

Understand customer needs

How will you extract insights from big data if you don’t have an idea of your customer requirements and your business needs? Before digging for insights, you need to focus on gaining qualitative customer insights. Thus, businesses must consider the challenges their audience is facing. This means interacting with people who use your product or service, recording their responses, and channeling those responses to improvise your product or service. Organizations must conduct studies and research for predictive analysis. Everything starts and ends with the customer. Thus, businesses must identify the key barriers that are preventing them from achieving their goals and then formulate decisions that will help achieve insights on how to maximize customer satisfaction.

Focus on the right sourcing

While a company’s main aim is to build insights from a range of data sources, it is crucial to focus on the types of data sources that will aid in the progress. The perfect dataset doesn’t exist. Start with analyzing data from a data mart. Most businesses are confused when it comes to the difference between a data mart and a data warehouse. A data warehouse is obviously an essential asset in any company, but a small and selective data mart produces quicker insights and prevents you from getting mired in complexity. Over time, you can then broaden your horizon and focus on additional data sets.

Enhance speed and delivery

Speed is a key factor for productive action. For successful execution from insights, you need to act quickly. If you spend a long time discussing and analyzing big data in the hope of acquiring near perfect insights, all your efforts will end up futile. When it comes to big data and its analytics, it is crucial to focus on quick decisions and execution. Today, successful companies like Amazon and Microsoft have one thing in common - they make their decisions from 70% of relevant data available. If they, too, would wait for perfect information for perfect insights, their outputs and revenue streams would face the threat of paralysis. Information that is substantial should be used to drive insights and specific actions, rather than waiting for more comprehensive options. The mark of a successful business is when a business can embed data and insights into its core processes and its everyday decision-making. Over time, this integration will make businesses more receptive to bigger decisions and greater change. As McKinsey has stated in Forbes, this way companies can “sift through massive amounts of data quickly, run the right analytics, and provide relevant insights so people can take meaningful action.”