While talking about analytics, mostly we hear about going from hindsight to insight to foresight. What most organizations and leaders miss out in this is the way these insights are delivered. And by that, we mean the timeframe, accessibility and scale of these insights for agility and nimbleness of their business to make data driven decisions to thrive in the marketplace.
The goal of analytics is to unlock value from your organizational data and help make data driven business decisions for unique and sustained differentiation in the marketplace. The enterprises that thrive are the ones that can create a virtuous cycle to go from data to insights to actions at speed and scale. For these data-forward organizations, data powers all aspects of business.
Empowering executives in the boardroom to field employees to make these data driven decisions in real time with insights from data that are on-demand and self-served is the future enterprises need to work towards.
Organizations are at different levels of maturity in this journey. Many are at a point where they have data warehouses on cloud and are leveraging analytics capabilities, however, it is not sufficient, not agile enough and not self-served. Data teams spend hours of effort and burn millions of dollars to create and maintain analytics solutions that are barely useful to the business. In contrast, the data–forward organizations work on building tactical solutions quickly and spend more effort and precious budget on building platforms to enable timely and on-demand data driven business decisions.
The leap is often too big, and business needs interim solutions in the transient time to make business decisions. Hence, we recommend incremental journey to build towards the desired state. With advances in data and AI technology, the on-demand and self-served insights can be delivered faster, with higher quality, and at scale.
Maturity of Analytics solutions in terms of the way they are delivered can be categorized into four stages. In this blog, we will take a deep dive into each of the four stages. Note that we are assuming a starting point with the organizations having at the least a data warehouse that consolidates structured enterprise data.
Companies often start their analytics journey with a data warehouse that is on premises or on cloud. The structured data collected from different business applications are converted into reports and dashboards that may be customized for Business Units (BUs). At this stage organizations begin to realize value from their analytics capabilities although they hardly have any agility or enablement of business users. In terms of operational agility, extensive IT reliance means frequent back-and-forth between departments and analysts, prolonging decision-making time.
This is where there is a need to for enhanced analytics capabilities that empower the business decision making.
At this stage, the organizations seek more value from their organizational data with enhanced analytics capabilities. There is still heavy reliance on central IT and data teams and typically these teams are busy churning out and enhancing analytical needs of business units. However, the speed and scale of delivering these analytics solutions to the business for helping with business decisions is still limited and is a function of capacity and agility of central data team.
The solutions though have typically evolved with business decision makers having the availability of configurable analytical solutions that may as well provide drill down features.
There can be an additional layer of capabilities added that provide the ability to the users to have concise summary of the data and insights. The natural language interface may manifest itself in terms of limited ability to do the question-and-answer type interaction with the dataset and the reports.
This is a good start; however, most organizations struggle with the rigidity and inflexibility of their solutions in terms of access to data on demand and limited organizational IT or data resources.
Around 90% of organizational data is unstructured and full of trapped value. Businesses cannot afford to wait to be more strategic and need to invest in platform capabilities that will leverage unstructured and semi structured data to fully enable business. Failing to manage your unstructured data results in fragmentation of your content, application sprawl, lost productivity, and, most of all, real business risk. Those who prioritize, manage, and secure unstructured data will gain a distinct advantage, and those who don’t will be left behind.
Organizations at this level of maturity typically invest in building out platform capabilities to leverage all forms of data across the enterprise and provide unified experience across all workloads to their users. Unified and strong data governance and security for AI and BI is at the heart of this platform. This sets them up for innovation and future enablement of self service and generative BI capabilities.
Organizations achieving this level of maturity are truly data-forward enterprises where data powers all aspects of the business. The data, analytics and AI are unified with consistent and centralized governance. Secured data sharing and full self-service and generative BI capabilities where business can independently leverage real time and on demand data for decisions without dependency on central IT or data teams. This truly achieves “BI for Everyone” state and power all organizational decision making. The advances in AI can then be leveraged to unleash its power for innovation and early but controlled adoption of cutting-edge features.
These organizations foster data-driven culture and encourage experimentation and adoption of technological advances that create a virtuous cycle of continuous optimization and innovation.
This journey is iterative and non-linear. Adapt the pace and sequence based on your unique context and progress. By taking an incremental approach with clear goals and strategic steps, you can unlock the transformative power of modern analytics driving agility, innovation, and sustained competitive advantage.
