How Companies Can Prepare Themselves for Data Science Adoption

Today, data science has become exceedingly popular and is a frequent topic for discussion all over the world. However, even though several organizations would like to get started with it, they seldom know how to go about it, especially in terms of achieving the business goals of an organization and other objectives which go beyond the departmental or lab experimentation.?

Another obstacle curbing adoption at large is the absence of a single clear definition of data science. This is mainly because data science is spread across several fields: software engineering, computer science, applied statistics, mathematics, probability theory, operations research, machine learning, and data visualization. Therefore, people in different fields have a different understanding of what the use of data science means.?

If as an organization, you are looking at adopting data science, then here's how you can prepare yourself for it.?

Identify Why You Need Data Science?

Firstly, identify why you need data science? Is data science relevant for solving your business problems? And if yes, does it justify the expenses involved? The motive here is to gauge whether the adoption of data science is worth the company's investment in terms of both time and resources. If you are in a dilemma, then here is a quick suggestion- focus on achieving the business objectives with minimal effort as opposed to adopting the latest technology.?

Identify Goals?

You cannot adopt data science just because it is a buzzword. You need to clearly identify the goals you want to achieve through data science – you can define the goals in terms of operational efficiency, cost-cutting, revenue increase, faster time to market, or employee engagement. For every goal, define the KPI to measure the success or failure. Only when you have such clear metrics defined, you will be able to track and measure the success of your data science initiatives.??

Operationalize Data Science??

Now, let us assume that you have identified why you need to adopt data science and that your company has the data and technology infrastructure to make this transformation. However, this does not mean that your employees know how to operationalize the process- they don't know what tools they need and what strategy they should be following. Most people still don’t know what data science means in practical terms. For this reason, clear-cut goals need to be defined for data adoption. This needs to be followed by creating an easy-to-understand actionable.?

Locate Relevant Data and New Data Sources?

As the term itself suggests, data is the primary part of data science. It works as the basic raw material that is required for its functioning. Now, the more data and the more data sources you have, the better it is. However, those have to be the relevant data sources. For this purpose, we recommend going beyond traditional transactional systems and look for interesting and relevant new data sources, such as sensor data, system logs, and social media data. In case they aren't relevant at the moment, look for ones that could be potentially relevant in the future.?

Collaborate Better?

A typical data science project involves data scientists, data analysts, business analysts, statisticians, and software engineers. Naturally, collaboration is essential to speed up the process. Therefore, you must first evaluate and then adopt the most suitable software solution or platform that enhances collaboration among these groups. Additionally, it is also recommended to promote collaboration through organizational constructs. For instance, you could form a team with representatives from each group and outline roles and responsibilities. Even monthly or quarterly reviews are a great way to encourage collaboration.?

Data Governance is Essential?

Companies need data governance to protect sensitive customer and other information. However, data scientists need access to as much data as possible to accomplish their goals. For both these needs to co-exist, it is vital to provide appropriate access. Data governance is never about restricting data access. It is about giving access which complies with enterprise and regulatory policies. Companies need to control data access, use infrastructure and application-level access controls to protect sensitive data, generate data lineage, usage reports and audit trails and automatically disguise sensitive fields.?

Focus on Data Security?

To ensure data security, data science platforms must provide data masking, data encryption in motion and at rest, several options to ensure appropriate user access. However, this must be done before starting the data science project, because changing architecture halfway through is difficult and complicated. Therefore, organizations need an appropriate data security strategy and detailed planning, to develop their architecture.?

Create a Data-Driven Culture?

Most organizations are not born model and data-driven. If your company is one of them, then your organization must undergo a cultural transformation if you would like to benefit from the adoption of data science. To create a data-driven culture, translate your top business problems into solvable data and modeling problems and create an effective data strategy around it. Next, ensure data science related efforts are aligned with your organization’s top business goals. Communicate this to your workforce at large. Even though most members of your organization may not be drawn to technical innovation, such a huge transition cannot be restricted to only those who are.?

The above-mentioned points will help you prepare for the process of data science adoption. After all, it's not about how quickly you adopt the latest technology but how well you leverage it that determines your progress.

We at Inteliment have been working in the Data Space for over 15 Years and have a team of Data Analysts, ETL Professionals, Data Scientists, who can help you navigate your data science journey. We have recently incubated our own data science tool - Rubsicape that can help you navigate this journey quicker.