Council Post: Business Outlook for Data Leaders in 2023

In a world driven by data, industry leaders are successfully extracting actionable insights through precise data management and analytics. This enables them to innovate quickly, develop better frameworks, and govern modifications more efficiently.
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“The goal is to turn data into information and information into insight.” – Carly Fiorina, former chief executive officer, Hewlett Packard. 

In a world driven by data, industry leaders are successfully extracting actionable insights through precise data management and analytics. This enables them to innovate quickly, develop better frameworks, and govern modifications more efficiently. With digital transformation set to sweep through every industry in 2023 and beyond, data leaders seem to be aware that they now have to equip their organisations to face uncertainty. New technologies that facilitate faster and more accurate access to insights are fostering new thinking across businesses. 

In the last few years, modern artificial intelligence, machine learning, data analytics, and robots have been driving the changing environment—forcing established business models to adapt.

Uncertainty is increasing as a result of the unparalleled global pace of change being driven by digital transformation. In the light of this, it is no longer possible for people, organisations, or governments to base their planning for the future on the assumption that things would mostly stay the same. Instead, they must investigate and get ready for a variety of different scenarios that represent possible changes and the fresh opportunities, along with the difficulties they might present. Using such a strategy can ensure that the strategies and policy frameworks created today are resilient and adaptable to the direction, velocity, and scope of changes that the digital transformation may bring.

Here are some ways to ensure businesses are ready for this uncertain future. 

Data Democratisation

The majority or all of the data was previously owned by IT teams. Business users would approach IT to request access to a specific data collection, and in response, IT would hand over a huge, disorderly spreadsheet. Data democratisation is the process through which an effective organisation makes data available to all employees and stakeholders—regardless of their technical background—and teaches them on how to deal with data.

The ‘data’ in data democratisation is any information you might be able to learn about your company or organisation. Data is ubiquitous, and it has the power to make every aspect of a company more efficient.

For instance, in a data democracy, sales benefits from marketing data governance and having access to marketing data to track the leads produced by a particular campaign. Additionally, marketers can obtain sales information to determine how effective a new marketing channel is.


Infrastructure, platforms, or software that are hosted by external providers and made accessible to users online are referred to as cloud services.

Cloud services make it easier for user data to move back and forth from front-end clients to the provider’s systems through the internet along with encouraging the development of cloud-native apps and the adaptability of cloud-based activities. 

Moving data analytics to the cloud has many advantages, including improved return on investment. These platforms were created by engineers to process and analyse enormous amounts of data quickly. Cloud analytics, in turn, assists companies in deriving value from their data for better decision-making, enhanced operations, and accelerated growth.

Companies that employ their tools effectively can get on-demand processing, storage, and data warehousing from cloud platforms like Amazon Web Services (AWS) for certain analytics use cases. Additionally, they can significantly shorten the time needed to build up a data infrastructure. Consequently, organisations can respond rapidly to assess the viability of a business hypothesis. If it turns out to offer new information, the solution can be improved upon and made into a product. If not, the resources can be promptly released so that the next hypothesis can be considered.

Real-time data

When digging into data, it is better to be aware of what is going on. This is the reason why real-time data is increasingly becoming a valuable source of information for data leaders. However, working with real-time data requires a sophisticated data and analytics infrastructure.

Data that is displayed as it is being acquired is referred to as real-time data. The concept of processing real-time data is currently prevalent in emerging technologies, such as those that provide mobile devices like phones, laptops, and tablets with convenient apps that give up-to-the-minute information.

But one of the most important use cases of real-time analytics is detecting potential fraud. For instance, fraudulent credit card transactions can be identified and prevented in real time. Although conventional analytical methods are capable of detecting fraud, they are excessively slow; the processing and analysis of the data may take hours. The detection of fraud in real time is possible using real-time analytics.

Agile data analytics

Agile data and analytics models make it feasible for data leaders to not only innovate but also differentiate and grow digitally. By integrating a variety of data analytics, AI, and ML solutions, data leaders are able to create a user-friendly and seamless experience.

Agile data analytics are tools and processes you can use to track the value your agile system provides to the various value streams in your firm as a whole. Agile data analytics can sometimes go further and enable you to use that data in reports that examine team performance, record the quantity of work performed, enhance your systems, and even assist you in making decisions.

Tracking relevant agile metrics, context, and measuring parameters can help you better understand the inner workings of your teams and value streams.

When considering your metrics, these are all very significant factors to take into account because measurements are meaningless without context. Agile data analytics assist in highlighting the significance of the data and patterns.

Integration of AI and ML in data management

The integration of automation in data management is empowering businesses to perform complex data-related tasks, along with ensuring regulatory requirements. This increased use of AI and ML solutions, as well as tools, is assisting them to stay relevant and compliant in an increasingly regulated data ecosystem. The upcoming ‘zettabyte apocalypse’—when the volume of data generated will overwhelm storage capabilities—can be navigated and monetised with the use of automated data management. Several businesses still struggle with data issues despite the fact that data quality and governance are the cornerstones of digital transformation programmes for contemporary business setups. Data governance, data quality, data catalogues, and data lineage must all be included in an integrated data management solution for modern data-driven enterprises if users are to be able to access data across the organisation to enhance workflows and operations. Beyond precise reporting, quicker analysis, and better data-driven decision making, firms gain a lot more from automating data governance concepts and related components.

Fostering a data-driven culture

By inculcating the concept of a data-driven culture within the enterprise, a key resource to implement ideas across every department can be envisioned. Today, the primary goal for business leaders is to empower their employees to actively employ data and bolster their daily work.

A data-driven culture will enable employees to use analytics and statistics to streamline their workflow and complete their jobs. Before introducing new policies or implementing notable changes at work, team members and corporate leaders gather data to gain insights into the implications of their decisions. Such data-driven decision making is supported by empirical facts, allowing leaders to make smart decisions that have a favourable impact on the company’s bottom line. Company executives may consult their instincts when making decisions, but they only take precise measures in response to what the data shows.

Based on findings from a McKinsey Global Institute study, MicroStrategy estimates that data-driven organisations are 20x or more likely to gain new clients and 6x more likely to maintain them.

With data analytics taking over technologies such as artificial intelligence (AI) and machine learning (ML), it is emerging as a critical game changer and offering data leaders a new perspective to grow. Data leaders are now transitioning to a data-driven business model, where data is assisting them in making decisions based on what we know to be true rather than solely sticking to instinct. With the upcoming recession, businesses need to be prepared for the worst. As Thoman Redman said, “Where there is data smoke, there is business fire.”

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.

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Biswanath Choudhary
Biswanath is a seasoned Analytics and an Operations leader with expertise in setting-up and scaling strategy, analytics, product, research and technology teams. Over the years, he has been instrumental in leading strong cross functional teams across : Asia Pacific, Europe, North America, Middle East and Latin America. He has a proven track record of managing multi-million dollar P&Ls, and delivering on double digit revenue growth, cost savings and EBITDA targets to build scalable businesses and teams. He has been instrumental in driving strategic business initiatives & Digital Transformation for Customers through Digital Products and Service Offerings. He has successfully delivered on Apps & services to Cloud - Azure, AWS, GCP, IoT, Data & Analytics Platform by integrating Artificial Intelligence, NLP, Machine Learning, RPA, Intelligent Automation, Big Data, Business Intelligence, Predictive Analytics & Reporting and latest technologies. Currently, Biswanath is the Chief Operating Officer at SG Analytics.

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