Council Post: Driving successful and resilient businesses at a time of great change in data practices

The world around us is evolving rapidly. The past few years have dramatically changed the way we work, and the increased pace of this change has forced organisations to rethink their business strategies, take precise decisions quickly, and find opportunities for growth. CEOs and executive teams around the world are laser-focused on delivering success now by connecting with their customers in new, simpler, and more cost-effective ways. To lead businesses through this change, leaders must take tough decisions around business growth and profitability, sustainability goals, strategies to withstand economic plunges, and more on an everyday basis. Data can play a huge role in addressing such challenges. Every day, 2.5 quintillion bytes of data is produced. However, the difficulty lies, not in the sheer volume of data acquired, but in determining the most effective method for processing, analysing, and drawing insightful conclusions from it. 

Business environments are more dynamic than ever

Maintaining resilient businesses that can drive success in today’s market is based on saving costs, reducing complexity, and increasing efficiency and the time to value—which is no small feat. Especially after the pandemic, offices are adapting to a more hybrid or work from home environment.

The increased pace of change has forced organisations to rethink their business strategies. This evolving business landscape is concurrent with the change in customer behaviour as they begin rethinking their needs and values. Additionally, every crisis—be it economic or environmental—challenges organisations to reduce costs and improve efficiency as they brace themselves for further change. Economies are significantly impacted by interconnected singularities such as rising economic disparity, increasing population pressure, ongoing security concerns, complex global economic transition, environmental hazard, and increased technological advancement, making the business landscapes more complex, riskier, and challenging to understand or manage. 

To lead businesses through such uncertainties, leaders must continually make difficult decisions surrounding business growth and profitability, sustainability goals, strategies to withstand economic plunges, and more. Data plays an instrumental role in helping leaders innovate and make these decisions promptly while also enabling them to enhance overall productivity, collaborate effectively, and accelerate impact in ways that haven’t been previously imagined. However, there remains a significant gap between the present and the future of the business world, particularly between data challenges and attaining valuable insights.

It’s easy to lose yourself in data for hours on end and even provide insights that are unimportant or irrelevant to your business needs. In order to acquire pertinent answers, you must ask the appropriate questions. If you want to act on your data, segmentation is crucial. You can begin delving deeper by grouping data that has a shared characteristic, such as a customer with comparable consumption patterns or schedules. Depending on the issue you want to address or the queries you want to address, you will be able to decide which category to study.

Closing the gap with automation, AI, analytics, and data

In order to bridge the gap between data challenges and attaining valuable insights, a unified strategy and platform with change management at its core is needed. The strategy starts at the data layer but needs to further include an analytics strategy, an artificial intelligence strategy, and an automation strategy to truly get data-driven insights for the organisation to work with.

An analytics strategy outlines how data will be gathered and used to support business decisions as part of a holistic strategic vision. It aims to clarify important reporting indicators. Creating a single source of truth is pivotal when one is developing an efficient data and analytics strategy.

To successfully create this single source of truth, it is necessary to be able to identify every data element’s origin, type, definition, and lineage. The key is understanding the source of the data and the modifications that have been undertaken. This enables the business to understand and convey what the data means.

Likewise, an efficient automation strategy offers organisations a thorough and integrated approach to adopt. Automation strategies that assess scope, reliability, and impact characterise both robotic process automation (RPA) and business process automation (BPA). Businesses are likely to implement automation solutions to enhance efficiency and apply human resources to other important facets. This is especially probable when one considers the shift in recent years toward giving employees more independence and autonomy at work. Without automation solutions, employees would still be trapped performing mundane and repetitive duties, thus making this transformation, more or less, impossible.

So far, it is evident that it is not only data acquisition that is crucial for businesses but also what follows after—the strategy, the process, and resource allocation. This implies that the strategy to process this acquired data to allocate resources efficiently and drive smarter decisions is imperative for modern enterprises. Technologies like Artificial Intelligence (AI) and Machine Learning (ML) are expanding data science capabilities to more people so that they can make better decisions faster, regardless of their technical expertise. Data-leading organisations—those with the most successful data cultures—see the business benefits of such a data transformation. 

“Data are just summaries of thousands of stories—tell a few of those stories to help make the data meaningful.”—Dan Heath

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.