Ketan Karkhanis, Head of Information Technology, Hikal
In a conversation with CIOTechOutlook Magazine, Ketan Karkhanis shared his views and thoughts pertaining to data-driven reporting as well as the integrity and security of data used for decision-making.
Ketan has over 21 years of experience in IT operations and is currently heading different aspects of Information Technology like business applications, networks, security and overall IT infrastructure. Mr. Karkhanis is also responsible to spearhead new technology and process initiatives ,oversee key project implementations and work closely with business teams to deliver value-based IT solutions.
In today's rapidly evolving business landscape, data-driven reporting is crucial,How can organizations leverage data-driven reporting to enhance their decision-making processes and gain a competitive edge?
Data-driven decision making is a process in which organizations use the data captured from various sources to drive their strategic, tactical, and transactional choices. It's about basing decisions on realistic evidence obtained from data, rather than relying solely on intuition or experience.
Data based decision making truly empowers organizations to make more informed, quantitative, efficient ultimately leading to improved outcomes. Data-driven decision making is fundamental in today’s digital world. Manifold proven benefits like increase in accuracy, gaining customer behavioral analysis, real time performance indicators, bottlenecks identification, true concrete and non-intuition-based insights etc. makes it a very important process for business to embrace and adopt quickly. Leveraging this process makes businesses strategic, efficientand more customer-focused, eventually gaining a competitive edge in the industry.
Effective data governance is essential for maintaining the integrity and security of data used for decision-making. What key components should be included in a data governance framework to support robust data-driven decision-making?
Establishing a well-defined data governance process &framework is necessary and the first step towards the success of Data-driven decision making. The key components to be included in the framework include the following such as define clear objectives & scope, clear roles and responsibilities on who are the data owners, data custodians, data consumers, Policies and procedures for data creation & usage, Data Quality management which includes metrics, validation, cleansing, Data security and privacy, Data Integration and Interoperability, Review impact analysis, Data Stewardship and Change Management and Regular trainings and handholding sessions.
All of this must be fully supported and endorsed by the Sr. Leadership team of the organization to bring about organization wide data discipline and sanity. Also need to ensure that top executives champion the data-driven approach and allocate appropriate resources for data initiatives.
The accuracy and reliability of data significantly impacts the quality of business insights and decisions.What strategies can organizations implement to ensure the accuracy and reliability of their data for effective decision-making?
Today’s businesses rely heavily on data accuracy. As the data size grows, the process of data management and governance becomes extremely important. Some of the key strategies organizations can implement to ensure data accuracy and reliability include Standardization & Democratization of data collection & data update processes, Regular data validation & cleansing process, Implementation of robust data governance policies by defining the right work flows and maker-checker mechanisms, Incorporate data observability to monitor and review data in real time, Roll out data literacy related training and awareness programs and Set up internal audit procedures to plan recurring data audits .
The shift towards data-driven decision-making has transformed traditional business strategies. What are the key benefits of incorporating data analytics into decision-making?
Data analytics can provide valuable data insights for business decision making such as correctly identifying customer needs, enhancing operational efficiency, improving marketing strategies etc. By utilizing various emerging and digital technologies, such as predictive analysis and machine learning, organizations can gain valuable insights from large volumes of data, leading to informed decision-making processes.
Data analytics can immensely help businesses understand their customers' needs and likings by analyzing their behaviors and interactions. Companies can use data analytics to analyze data such as purchase history, search queries, website interactions to gain valuable knowledge on what products their customers are interested in, payment pattern, seasonality and other preferences. Also helps in planning an Omnichannel experience to customers thereby increasing operational efficiencies.
Data analytics can also help businesses predict trends and market changes by analyzing data on industry inclinations and financial indicators to adjust their offerings and marketing efforts. This helps companies to improve their go-to-market strategies & to stay ahead.
With the increasing complexity of data, organizations face challenges in extracting actionable insights. How can companies overcome these challenges and ensure that their data-driven decisions are both relevant and actionable?
Deriving actionable insights from complex data requires an organized approach. Always a good start would be to clearly define the end outcome, goals, and the problems we want to solve with this data analysis. Next would be data integration wherein integration tools can be used to collect and integrate both unstructured and structured data. Subsequently, the data cleaning process which is crucial to remove inaccuracies, duplicates, and inconsistencies must be planned. This step ensures that all our analysis is based on high-quality error-free data. Depending on our objectives, we need to select appropriate analytical techniques like statistical analysis, data mining, machine learning, etc. to help uncover patterns, trends, and correlations in the data. Then would come the data visualization part wherein one of the visualization tools along with data warehousing/data lake must be used. Charts, dashboards, graphs, etc. created by these tools make complex data sets easy to understand and interpret. These are just some of the ways and means wherein complex data can be handled in data-driven decision-making processes.
Overcoming data complexity and ensuring relevant, actionable decisions involves several strategies such as applying robust data control which includes establishing clear data management policies, standards, and quality regulations to ensure data consistency and trustworthiness Leveraging Analytics tools for utilizing modern data integration and analytics tools that can handle diverse data formats and sources, Invest in data training to empower employees with the necessary skills to understand and utilize data effectively, Implement real-time data processing for implementing real-time data stream consumption and analytics to gain timely insights, foster a data-driven culture to encourage a culture where data is valued and used in decision-making at all levels of the organization and Safeguard Data Security and Compliance to manage compliance, security, and governance risks to protect data integrity and privacy.
By applying these strategies, companies can successfully manage data complexity and make decisions that are both relevant and actionable.
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