How a Well-Implemented Data Analytics Strategy Will Directly Impact Your Bottom Line

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How a Well-Implemented Data Analytics Strategy Will Directly Impact Your Bottom Line

Data Analytics

How a Well-Implemented Data Analytics Strategy Will Directly Impact Your Bottom Line

September 8, 2021

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The data analytics market was estimated to be USD 15.11 billion in 2020, and is expected to grow from 2021 to 20-28 at a CAGR of 25.7% to reach USD 74.99 billion. This tremendous growth is being driven by the need for advanced analytics to identify future trends in trading, energy consumption, and traffic conditions, and even political developments as well as the climatic conditions in an effort to improve operational efficiency and respond accordingly. It can help businesses make informed decisions to improve profitability, customize solutions to improve customer delight and strengthen competitive advantage. To leverage the benefits, not only has data analytics been adopted by the various industry segments such as manufacturing, banking, healthcare, and professional services, even government agencies are turning to analytics to improve their service capabilities.

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But, we must also bear in mind that there is many a slip between the cup and the lip. As many studies such as those from Gartner and the Economist show, the failure to convert data analytics projects to achieve significant growth is quite high. That is because, many approach it like a technology project, investing in best-in-breed solutions but failing to create a strategy or a business case to justify those investments.

Even when creating a business case, rather than having broad, comprehensive goals, it is best to prioritize the investment categories. To experience tangible top- and bottom line benefits, the investment decisions should be based on your insights maturity. Forrester recommends pursuing insights-driven business (IDB) transformation, where enterprise data is transformed into insights that can trigger actions and result in desired business outcomes. For this, along with tactical investments, it recommends making strategic investments into people, process, data, and technology.

Creating the Strategy–The Best Practices

Indium worked with a top Oil & Gas consulting firm that leverages IoT-driven analytics to help

Oil & Gas companies to enhance and optimize their operations. Indium’s expertise in Big Data analytics enabled it to develop a strategy that involved a three-phase implementation. The client was able to experience:

  • 50% reduction in time-to-market (TTM) by leveraging the product
  • Minimized downtime costs and maximized productivity using predictive maintenance and improved failure prevention

A clear definition of the goal, a clear roadmap for the project, and various other factors contributed to Indium’s success in helping the firm meet its objectives. In our experience, often data analytics projects do not deliver because of:

  • Not defining the goal to be achieved
  • Not creating a solution or design for the objective
  • Not translating data science insights into actions to change the way business functions

Some of the best practices that Indium swears by to overcome these challenges and ensure improvement in bottom line from a well-implemented data analytics strategy include:

  1. Look for Quick Wins: Start small and identify critical but short projects to begin with. These projects may bear fruition within a month or two and impact your revenues and efficiency significantly.
  2. Identify a Champion: It is important for identifying an owner for these different projects who will ensure the goal is achieved at the local level.
  3. Assess Available of Data: Data is a broad term. While there may be organizational data available, it needs to be clean and relevant for the project you have undertaken. Also, enriching with external data will enhance the accuracy of the insights.
  4. Establish KPIs: Establishing metrics to measure the impact of the project serves to objectively assess the success of the initiative.
  5. Periodic Reviews: This is very important to know if the project is progressing as planned or needs a course correction to achieve the desired outcomes.
  6. Create the Right Team: Just as important as the technology and data are the people involved in the project. It should be cross-functional and have a sponsor who drives the project.
  7. Involve IT: To scale up from the Proof-of-Concept (POC) stage, it is essential to involve the IT team so that the necessary resources can be made ready. An ideal team should have data scientists, data engineers, architects, visualization experts, and data science storytellers either in-house or outsourced.
  8. Getting the Right Tools: Yes, tools do matter. The composition of your team will determine whether to go with visual modeling or to go for coding languages such as Python or a mix of both. Whether to go for multi-cloud, the computational power needed, how much data volume and velocity you need to provision for will also have an impact on the outcome of the project. Use Agile: Creating an Agile Data Science approach can help you implement projects fast, fail fast, and manage risks better to achieve the desired results quickly.
  9. Plan for Scale: Not enough provisioning for scaling from POC to scale is one of the primary reasons for the failure of data analytics projects. Allocating sufficient budgets and resources is essential to see the project achieve its full potential. This should also include identifying operational issues when you scale and creating a roadmap right from the start.
  10. Trigger Action: Often, the insights drawn from data analytics projects may not get converted to actions. So along with insights, predictive and prescriptive analytics are essential to achieve the end result.

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Indium – To Meet Your Data Analytics Objectives

Indium Software has more than 20 years of experience in cutting-edge technologies, data science, Big Data, and data analytics. Our process-oriented approach along with technical expertise and cross-domain experience help us in assessing each project we undertake critically to create bespoke solutions that meet the individual needs of the organization. For data analytics projects too, we first study the data, the objectives, and the technology of our clients before we create a strategy that leverages existing capabilities and recommend complementary solutions to protect their existing investments. The roadmap also ensures that the projects can scale as they fulfill goals in the near term and can be leveraged even as the client grows from strength to strength.

To know more about Indium and our capabilities in helping you achieve your data analytics goals, contact us now.