Blogs5 Steps to Become a Data-Driven Organization

May 19, 2022by Shashiraja
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The increase of technological advancements in business has necessitated the increase of data science, in particular data compilation and analysis. more data these days but the amount of data you are handling is only as important as the information you are churning out. Businesses are dealing with exponentially more data these days, however, the amount of data they handle is not as important as the information they infer. More data and less information have been a consistent problem for businesses. In this age of digitization, almost every aspect of the business has a digital footprint. Some significantly more than the others. This presents a unique opportunity where potentially all information can be reliably processed to take tactical and strategic decisions from a position of knowledge. Good data can facilitate hedging, forecasting and other key corporate activities.

With all of that in mind, it is undoubtable that companies must lean more heavily on data in the upcoming years.

let’s look at the 5 ways to become a data-driven organization.

1- Have a system in place for obtaining data The first step to being a data-driven organization is to have an in-house team that deals with obtaining data relevant to their current endeavours. This activity can be outsourced to data scientists or establishments. While outsourcing to partner companies ensures they have a track record of making accurate inferences from a given dataset. If you’re looking to conduct your company’s data science in-house, then lookout for the following proficiencies from your potential employees:

– Critical thinking
– Machine learning, deep learning, AI
– Coding
– Risk analysis, process improvement, and systems engineering
– Problem solving and good business intuition

2- Incorporating data into your business strategy The data side business is vast. It ranges from simple analytics to AI, Big Data, Data Analytics, and Data Sciences. Many companies have implemented AI, Big Data, and Data Analytics in their projects for years. The implementation of these technologies come with enhanced efficiency and improved productivity in the projects.

In simple terms, incorporating data in a business strategy for security means strengthening the data management processes. Making sure that all the data and information are accounted for. You just need to work for better data control, and you can accelerate your business strategies by empowering the right people with the right information. For example, giving marketing people the data regarding the most popular products among your customers could help them make a great campaign.

A proper data strategy is required for ensuring security in a business strategy these days. The data strategy comprises various aspects. These aspects include data integrity, data quality, data regulatory compliance, and data governance. Data strategy would provide you not only control over your data but you would also be able to enjoy flexible options to utilize your data resources for various business operations.

3- Encourage and implement the use of data and data analysis at an organization-wide level Enable everyone at your company to use data daily. While the Analytics team owns the data stack, every employee needs the opportunity to use relevant data in their existing workflows. At the org level, all departments should share the same sources of truth and work toward the same shared metrics.

Breed familiarity with handling data through proper organization-wide data training. Train everyone at your company to use and interpret data accurately. Recognize that no one is born data literate and implement classes to teach everyone to interpret and use data properly in their daily jobs. All employees should understand how to determine if they’re using metrics, goals and conclusions correctly.

4- Act upon the inferences made through data analysis It’s all well and good to be able to collect tabulate and analyze data. If however, the company fails to act upon these inferences, the entire exercise is useless. Act on predictions of the future. Spend your time using data to make testable, tactical predictions, then take actions based on those predictions and feed the results back into your process to improve future decisions. The data-driven decision engine looks a lot like the scientific method. Historical analysis should be focused on learning why something happened, not just reporting what happened.

Make all decisions with an evidence-based culture. Make sure data is trusted throughout the organization and included in most status-level communications. While “gut feels” might provide initial areas for investigation, decisions should be made based on science. Ready analysis can then support the business by being relevant, informative, and actionable.

Data and statistics often get disregarded as if they are some sort of arbitrary number that does not accurately co-relate with reality. Data, however, is nothing more than a record of events that have taken place. Or a reflection of the current opinions within your consumer market.

5- Constantly strive to improve your company’s data efficiency Know that you’ll never finish and embrace that. Understand that your analytics team has more work than it will ever be able to finish, and be rigorous about prioritization. Make sure your organization has a continuous improvement mindset and is always looking for opportunities to optimize existing processes and capture new data. Invest in collecting data, even if won’t be immediately useful. Spend the resources to invest in knowledge without knowing if it will pay off. Collect data from wherever it is generated, not just from easily accessible stores. And make fixing data collection errors and outages a high priority. Constantly improve your data tech stack. Expect to frequently update or expand your technical footprint. More data from more sources requires more data tooling over time. Concurrently, invest in documentation and unification projects to minimize sprawl.

With the market increasingly moving into the digital sphere, it is becoming increasingly necessary for organizations to develop more efficient ways to conduct their business. Embracing data science and data analysis is a sure-fire way for a company to become more efficient across all axis’ of its performance. Including customer behaviour, organizational practices and policies, product performance, and employee performance.

 

 

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