What it takes to be a truly data-driven organization

What It Takes to Be a Truly Data-Driven Organization

Data Analytics is the pivot around which the modern-day business landscape revolves. Most organizations today are investing in the latest technology, hiring high-skill employees, and leveraging data analytics – but does that make them truly data-driven? Not entirely.

Simply having access to data is not enough. To achieve true analytics success, businesses must ensure their data is accurate, actionable, and aligned with their goals. According to a study by NewVantage Partners, only 26.5% of companies report that they have been successful in creating a data-driven organization, despite 97% of them investing in big data and AI.

These statistics suggest that while many companies are investing in big data and AI, they are struggling to fully leverage these technologies to build a data-driven organization. Lack of clear strategy, poor quality data, lack of skilled talent, and resistance to change are some of the major reasons that prevent companies from extracting maximum business value from their investments in these technologies.

How Can You Ensure You’re Leveraging Data Analytics to the Fullest?

The use of analytics has become rather common across all industries in today's ever evolving business environment. By leveraging data to drive insights and decision-making, businesses can gain a competitive edge and achieve better outcomes. However, the effectiveness of data analytics hinges on its proper utilization.

Achieving successful analytics entails more than simply having specialist data scientists working in silos. It necessitates a shift in organizational thinking towards one that adapts well to the changing dynamics of the business market and technology that streamlines operations and measures revenue growth.

When data is collected, processed, and analyzed effectively it can become a fundamental driver of success for businesses. The effective utilization of data analytics depends on several crucial factors, starting from identifying business objectives to presenting insights, and incorporating feedback for continuous improvement.

Best Practices to Extract the Most Out of Data Analytics:

Prashanth Southekal – an author, professor, and head of DBP-Institute (Data for Business Performance) highlights the three crucial tenets of achieving analytics success in an article published by MIT Sloan School of Management:

• Adopt an Analytical Perspective to Defining Business Objectives: Identify key business objectives and align your analytics efforts with them. The idea is to adopt an analytics perspective on data by aligning the questions posed by the business with the appropriate data types required to provide answers. This helps in prioritizing what data to collect, how to classify and analyze it, and how to use it to drive insights and decisions. Classifying business data by type, such as reference data, master data, and transactional data, and developing conversion rules to transform data for analytics can also be really helpful.

• Effective Data Governance and Sourcing: To ensure the quality, ownership, and security of data, organizations must establish data governance policies and procedures for data collection, storage, and usage. Rather than waiting for perfect data, businesses can strategically source data, balance the cost of acquisition with its value by using sampling or feature engineering to enhance data quality, and explore data collection methods that align with the business objectives.

• Make Actionable Analytics the Goal: Businesses should not only focus on running analytics projects to gather insights from their data, but they should also aim to turn those insights into actionable analytics products that can improve overall business performance. Instead of just conducting one-off data analysis projects, businesses can rather build analytics products that can be used continuously to drive business decisions and outcomes. These products could take the form of dashboards, reports, predictive models, or other tools that provide valuable insights to business stakeholders.

These three tenets by themselves are not sufficient to leverage data analytics effectively. They should be looked at as fundamental prerequisites for organizations to become truly data-driven. To complement these principles, businesses can also follow some general rules to achieve successful outcomes. These include building high-performance teams, focusing on descriptive analytics and key performance indicators to build data literacy, refining analytics models continuously, and using data storytelling to promote insights.

In conclusion, analytics success requires a multifaceted approach that includes technology, processes, people, and culture. Whether you're a small start-up or a large enterprise, it is clear that data analytics is the elixir to succeeding in today’s complex business world.

What to look for in high value data talent - Quick notes

Problem Solving Skills

DS professionals must be able to identify and describe problems in actionable ways. They must be able to understand the environments in which the problem exists and how it impacts stakeholders impacted by the problem. They must also be able to apply their DS knowledge and skills to respond with viable business strategies, and not just data science strategies. By understanding the business context, you can tailor your approach to the specific needs of the organization.

To ensure data science strategies are transformed into business strategies, it is essential for data science teams to collaborate with other departments within the company. This can be achieved in a number of ways, including:

Business Acumen

While business leaders desire specialists that can devise DS solutions, they want to see that DS professionals can formulate strategies within the language and context of the business problems. Specialists are also expected to answer questions in ways clients understand, so that they in turn, sell it to their stakeholders.

Communicate Clearly and Effectively

As a data science specialist, you need to be able to communicate your ideas clearly and effectively. This means using language that business stakeholders understand and avoiding technical jargon. You should also be able to explain your approach and results in a way that is easy to understand.

Provide Examples and Visualizations

One of the best ways to communicate your ideas is through examples and visualizations. By providing concrete examples and visualizations, you can help business stakeholders understand the potential impact of your work. This can also help you build credibility and trust with the hiring leaders.

Be a Team Player

Data science projects often involve multiple stakeholders and team members. As a data science specialist, you need to be able to work effectively with others and be a team player. This means being open to feedback, collaborating with others, and being willing to adapt your approach based on the needs of the team.

Focus on Results

Ultimately, hiring leaders want to see results. As a data science specialist, you need to be able to deliver results that have a real impact on the business. This means focusing on the outcomes of your work and being able to demonstrate the value of your contributions.

A Commitment to Continuous Learning

Businesses are looking for data science professionals who have strong problem solving skills, business acumen, technical expertise, communication skills, and a commitment to continuous learning. By developing these skills and qualities, data science professionals can position themselves as high value talent in a highly competitive industry.

By following these tips, professionals can impress hiring leaders as a data science specialist and stand out. Remember, it's not just about technical expertise - it's also about having the ability to strategize within the language and context of the business problems.

📊 Future Focused - Supporting Grassroots of Data Science and AI

With new technologies sprouting in both Data Science and AI, we are seeing a major increase in interest for these industries at the Grassroots level. Organizations are always looking for specialized talent in directed subjects. Unfortunately, like many other emerging industries, we are seeing organizations struggle to keep up with industry standards due to talent shortages and a skillset disconnect. What needs to change? How can we all contribute to helping those driving the future of this fascinating industry?

Benefactors are required to support those looking to enter the industry, whether this be through a direct recruitment service or an educational institution such as a college or university. Many industries are turning towards charities for support at this level, however, this is something we are yet to see for data science sectors.

For those starting a small company or service, whether that be AI related or not, the initial income they receive from their service is crucial for the future of their development. Medium has launched “QuickFund AI” to minimize the risk of payments and speed up the initial process. This is a great example of how AI is being used across multiple industries to support small businesses and those entering a competitive profession.

TeamEpic is supporting graduates in India to help them reach their dream job and enter the industry with the expertise required. We discovered that many graduates have a broad range of skills when leaving their full time education but employers are looking for a specific skill set to fill a role. This is where TeamEpic will open a dialogue between the organization and the individual to ensure they have the correct and sufficient training for the job.

Perhaps this is a larger issue on the education side of the industry. Perhaps we need more directed courses that target the specific skill sets required for these high-end roles. Perhaps this support needs to come from the organization itself, rather than relying on institutions to have provided the correct support.

The answer may be a congregation of these conclusions. There needs to be further dialogue between multiple sectors of Data Science and AI to allow those in the industry to unlock the full power of technologies available. Someday we may all be reliant on services that are a product of the Data Science and AI industry. By supporting the grassroots of our future, we can all see positive benefits down the line.