Badri Ramakrishnan, Technology Leader of lululemon, shares his thoughts on maximising corporate value through data-driven choices 2023

Data analytics is a fast-expanding and more vital subject for businesses seeking a competitive edge in the current data-driven corporate climate.
This is an exclusive interview with lululemon’s Technology Head, Badri Ramakrishnan, conducted by the Editor Team of CIO News.

Badri has successfully managed programs to upgrade outdated systems, develop novel digital solutions, and enhance business processes with cutting-edge technology. Badri is a technology industry thought leader with a passion for harnessing technology to generate good change and enhance business outcomes.

What are the current data analytics statistics?

Organizations increasingly rely on data analytics to get insight into their operations and to make data-driven choices. Recent figures project that the global market for big data analytics would reach $103 billion by 2027, with a compound annual growth rate of 10.9% from 2020 to 2027.

Moreover, according to a survey done by International Data Corporation (IDC), the average firm currently invests more than $19 million annually in big data and analytics activities. This investment is motivated by the growing availability of data and the necessity for enterprises to make better-informed decisions in order to remain competitive.

Statistics indicate that data analytics is a fast-expanding and increasingly vital subject for businesses seeking a competitive edge in today’s data-driven corporate climate.

Could you comment on the leading issues and solutions in data analytics?

Managing enormous quantities of data from many sources is one of the most difficult aspects of data analytics. This can result in problems with data quality, accuracy, and completeness, which in turn can affect the dependability of any insights derived from the data. To overcome this issue, businesses may engage in data cleansing and standardization procedures, as well as technologies that enable them to combine and analyze data from diverse sources in a unified manner.

The requirement to extract relevant insights from the data, which involves complex data modeling and analysis tools, is a further obstacle. This can be especially difficult when working with intricate or unstructured data. To overcome this issue, businesses might invest in machine learning and artificial intelligence technologies that can automate the data modeling and analysis process.

Lastly, there is the difficulty of protecting the privacy and security of data, especially in light of growing worries around data breaches and cyberattacks. To solve this issue, businesses can invest in comprehensive data security measures, such as encryption, access restrictions, and monitoring tools, and adhere to any data privacy rules.

Which top data analytics trends are organizations need to adopt in 2023?

  • Increasing use of artificial intelligence (AI) and machine learning (ML) in data analytics: Increasingly, AI and ML are being used to automate and improve data processing, and this trend is likely to continue in the coming years. In order to keep ahead of the curve in data analytics, businesses may need to invest in these technologies.
  • Emphasis on data visualization and narrative: As the volume of data created continues to rise, companies will need to develop new methods for successfully communicating insights. Complex data may be made more accessible and useful for decision-makers using data visualization and narrative strategies.
  • Use of natural language processing (NLP) and other sophisticated analytics techniques: NLP is used to extract insights from unstructured data such as social media postings and consumer feedback.

    Although data visualization is suggested as the final step of the analytics process and aids firms in perceiving large amounts of complicated data, how can data visualization facilitate decision-making by utilizing visually interactive methods?

  • Organizations may obtain insights from complicated data and make better-informed decisions through the use of data visualization. Using graphically interactive techniques, data visualization may facilitate decision-making for businesses in the following ways:
  • One of the primary benefits of data visualization is its ability to simplify complicated data, making it easier for decision-makers to comprehend and analyze. With charts, graphs, and other visualizations, enterprises may show data in a manner that is simple to comprehend, even when dealing with massive amounts of data.
    Permitting interactive investigation: With interactive data visualization tools, decision-makers may study data in real-time, filter and dig down into individual data points, and pose “what-if” questions to see how alternative scenarios may affect results. This can aid decision-makers in gaining a deeper comprehension of their facts and making more informed choices.

Could you list the top data analytics best practices that guarantee organizational success?

Before beginning any data analytics project, it is essential to outline the project’s goals and objectives with precision. This will guarantee that the analysis is targeted and connected with the organization’s broader strategy.
Engage stakeholders: Engaging stakeholders throughout the data analytics process may aid in ensuring that the analysis is relevant and beneficial to the company. This may entail requesting input on project objectives, releasing interim results, and soliciting comments on the final analysis.

Ensure effective communication: Effectively communicating the outcomes of the study is essential for ensuring that the insights are implemented. This may entail presenting the results in a clear and straightforward manner, utilizing data visualization tools to assist convey complicated information, and collaborating with stakeholders to establish a plan of action based on the insights.
Assure data quality: The success of any data analytics project is dependent on the quality of the data being analyzed. It is essential to ensure that the data is accurate, comprehensive, and pertinent to the project’s objectives.

A systematic approach to data analytics can aid in ensuring that the analysis is robust and consistent. This may include adhering to a particular methodology or framework, such as CRISP-DM (Cross-Industry Standard Procedure for Data Mining), which consists of a number of phases that guide the analytical process.

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