How To Build A Data-Driven Culture

Leading companies around the globe are pushing to transform themselves to become a data-driven company.

Next to branding, data is seen as the most valuable product that a company creates. With data, companies get insight into customer behavior, operations, their channels, in fact all of their touch points.

Here are 6 Steps that companies are using to lay the foundation of a data-driven culture within their organization.

The first and the most obvious step is to collect the data and store it.

Most companies already have an established process to collect basic data about their business to understand the customers and operational issues. It is better to collect data without judging its value. Later, when we try to solve other business problems, the data you’re discarding may become the missing piece of another puzzle.

Try to gauge the velocity at which you’re collecting data. You might be collecting your website analytics data in real-time, but you might be receiving the results from your sales and service follow up surveys once a day, or less frequently.

Some mission critical applications might have a problem sending data, and report with gaps can show you what you need to fix.

The second step is to create standard reports at standard times. This helps you understand the past and allows causal analysis of any problems might arise.  A Fishbone diagram is a popular tool to analyze these kinds of problems.

At this point some companies find they are generating a lot of data, so they create a data wearhouse and develop ETL processes to handle their data better.

There are excellent reporting tools available that will help you get the reports you want with very little or no coding. Tableau and Microsoft Power BI are relatively easy to use reporting tools.

The third step is when companies realize everything is data, including images, videos, mails, chat texts, website contents etc.

Some companies do not of this as data because they are in unstructured format and difficult to use.

Today there are lot of tools available now to extract the data from these contents and convert it to a structured format to use it easily.

When companies start to treat everything as data then the size of the data grows even more. This is a good time to look at cloud platform so that you get the option to auto scale exponentially in terms of storage, processing power and networking speed and bandwidth.

Big Data systems are designed to hand the velocity, veracity, variety and volume of the data very well. A few of the Big Data systems in the market are Google BigQuery, Apache Hadoop, and Amazon Redshift.

Companies that have digital transformation projects should focus on creating a synergy between legacy systems and modern cloud platforms. For this reason, companies need to find the right Big Data solution and the right set of ETL tools to offer a strong integration between on-premise and cloud platform integration.

The fourth step is to democratize and socialize the data for internal users, with the right security and privacy measures put in place. At this point data will start to serve as fountain of innovation.

Instead of providing raw data access to the users, build advanced analytical solutions that will fetch the required data for the analysis that the users want.

This allows users to run ad-hoc reports to understand problems deeper, figure out dependencies, and come up with innovative solutions.

 It might be good time to introduce Natural Languate Processing (NLP) applications to help users interact with the system more naturally. Tableau has an excellent NLP engine that helps users query data and get results via voice. The more mobile your employees and customers are, the more NLP can help your business.

The fifth step is to focus on correlation analysis instead of causal analysis.

Users should be taught to look for how strong are the relationship between the variables they are looking for. E.g.: How strong is the connection between sales of printers, its service care pack, and its cartridges?

This also requires a shift in mindset of the users to treat data to find patterns rather than to extract absolute metrics out of it. A data science team can now help you run correlation analysis, anomaly detection, and recommendation systems.

Companies which enter this phase have a dilemma as to whether they should focus on refining their analysis or collecting more data.

It is often better to focus on collecting more data with huge veracity and sift it through a tagging process. It is very important to find out the extremes and outliers in your data that will help validate the analysis or bring out its limitations.

The sixth step is a very big milestone. This is where you use the data to predict future.

Predicting the future makes your organization proactive and curious about the forecasts that spawn out of your predictive analytics system.

A proactive organization will improve the customer experience exponentially and solve or mitigate problems before they arise.

There are lot of AI platforms that can be trained using your data to predict the output of your problems. Have a strategy of what type of predictive analytics and prescriptive analytics system you want for your organization and the go about choosing the AI platform for your needs.

One important point to remember is the law of diminishing returns when it comes to improving the accuracy of the output beyond 90% in some problems.

Do you have a roadmap to transform your company into a data-driven company?

The right digital partner can help you build a roadmap for this transformation, plan a strategy to execute, and help you pick right tools and technologies and guide you in your journey to become a data-driven company.

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