Even if you are not a data scientist, your questions can impact the way analytics team works

Data analytics system in a company is of great importance to both the data scientists and managers/executives who have no data science background. For people who work at high positions in an organization without data science knowledge, it is not necessary to understand the inner workings of an analytics project, but they should also know whether the right things have happened and the results are suited to the business problems. Henceforth, even if you are not a data scientist, it is mandatory to have certain questions in mind for the analytics team that will help the company grow.

Covid-19 pandemic has highly changed people's behaviour. The analytics team is having a hard time understanding the changes and reciprocating it with the right products. Trends are constantly changing and the datasets are not as useful as you think if it is not used properly. Leaders with no data science background also contribute to analytics improvements. He/she should know that advanced analytics is mostly about finding relationships between different sets of data. The leader's first job is to make sure the organization has the tools to do that. Deloitte in a recent survey named 'Analytics Advantage Survey' revealed that the greatest benefits of data analytics teams are better decision-making, according to 49% of business leaders. Yet, a mere 9% reported better financial performance as the greatest benefits. A successful organization takes data and develops new products, gain invaluable insights and create new opportunities. Henceforth, a leader should take part in all these roles. If you are not a data person, you can still train yourself to ask valuable questions that are necessary to build an effective data environment. Analytics Insight has gone through the details and found five mandatory questions that a leader should ask his/her analytics team.

How can we create the data we need from the datasets we have?

Data analytic decisions rely on large data sets called 'big data' of a company. But the data you have in hand as raw content might not be directly connected to the decisions you make. Henceforth, businesses need to think hard about which variables or combination of variables are the most salient for key business decisions. For example, insurance providers are piloting a telematics program that racks policyholders' driving patterns to detect their risk. For the analytics team, transforming these data into their most usable form might require the creation of new composite variables or scores from the existing data. Even though big data is a complicated stuff, the results we get through it solely depend on how we leverage the right dataset for a relevant process.

What areas of business do we need to support with more data-driven insight?

Leaders need to understand that data analytics tasks are performed to make future decisions. Unfortunately, most of the analytics teams spend their time to answer ad-hoc requests and questions focused on the past. With the help of artificial intelligence and machine learning, the analytics teams are doing their job of sorting the datasets and finding the relevant answer. But their focus shouldn't be general or vague. Analytics team should figure which department or session of a company needs more data-driven decisions. A leader has the responsibility to know if the analytics team rightly supports data-driven insights.

What techniques are we going to try?

Techniques such as machine learning, data science, programming, statistical methods, etc. are leveraged by the analytics team to get the right solution. However, not all datasets need similar kind of techniques. For example, if you take sales into account, it can mean sales volume in units, sales revenues in dollars, or a binary purchase of yes or no. These heavily impact the way analytics team take a decision. Henceforth, never forget to ask what kind of techniques the team used to come up with the right answer.

What are the results and recommendations?

More than sorting the data and finding the right analytics solution, following the results is what takes the company to a higher position. Analytics is an ongoing process, and it is important to iterate and know that sometimes it'll be right and sometimes you'll need to learn. But a leader should never agree without questions to the analytics team. If the total behaviour of the consumer feels different from the analytics decision, asks how the team came up with this answer.

What kind of data visualization tool do we have/use?

Data visualization is as important as the effort an analytics team put on to get the right answer. If data visualization is vague and has no good content, then the hard work by the team will go unnoticed. Henceforth, it is mandatory to use the right tools. Leaders should know what tools the analytics team is using.