Data has always been central to running a business. And thanks to rapidly advancing technology—and especially the cloud—organizations can now bring together more types of data, in real time, and make it more useful to everyone across businesses.
Cloud’s ability to scale economically means it can handle huge amounts of data and power the rapid insights needed to help organizations shift from reactive to prescriptive operations. And as the latest generation of artificial intelligence capabilities come of age, the pace of investment has increased dramatically.
How are enterprises managing data & analytics today?
Two different approaches have emerged: “top-down” and the “bottom-up”.
A top-down approach “institutionalizes” a company’s vision through a comprehensive long-term strategy. It emphasizes organization-wide coordination, economies of scale, and alignment with the business model. It aims to provide an enterprise-wide prioritization of a “single version of the truth”, and leverages cloud platforms to rearchitect data and provide access to leading analytics services.
The advantage of a top-down approach is that it enables strategic enterprise-wide decisions, can help attract and retain top-tier talent, and provides a truly leading-edge architecture. However, the sheer scale of the transformation required means it can take longer to bear fruit—and can be more susceptible to losing focus.
A bottom-up approach is characterized by organic agility and speed to insight. Business units or individual teams build or buy their own solutions to meet their immediate data and analytics requirements, faster.
Bottom-up approaches offer more sustained innovation. Insights are typically more tightly integrated with processes, and more closely aligned with the needs of business users. However, it can mean the organizational approach to analytics lacks overall coherence, resulting in a fragmented/siloed architecture that limits opportunities for data-led innovation at scale.
The solution? Operate at multi-speed
In this report, Accenture proposes a more balanced, multi-speed approach to data-led transformation. By creating a middle layer in their organization—such as a Rapid Insights Lab (or “RIL”)—enterprises can chart a path between top-down strategy and bottom-up agility.
Staffed by a critical mass of data and analytics experts, an RIL is able to provide rapid “white glove” data insights to the business as a service. That means it can offer more strategic data and analytics outcomes much faster than a typical top-down transformation:
Complex data science insights available to everyone in the business, faster
Analytics outcomes available in a form the business can quickly use
Data literacy and data-driven decision making promoted across the organization
Ability to test out and de-risk analytics concepts before significant investment committed
By creating a middle layer in their organization—such as a Rapid Insights Lab (or “RIL”)—enterprises can chart a path between top-down strategy and bottom-up agility, and make better decisions with data.
How to get started with rapid insights?
The report includes several key suggestions for setting up an RIL, including:
Consider both your organization’s current data maturity and type of data science problems you want to solve.
A balanced and appropriate mix of technical and business domain skills is key.
The RIL should be able to scale operations up and down as needed.
A delivery platform will eventually be needed to enable rapid prototyping and spin up new features on demand.
The RIL stays true to its original mission by transferring out proven prototypes for scaling up.
Time to shift gears to multi-speed
The multi-speed concept enabled by an RIL is a proven approach that may deliver differentiated results. Accenture believes it can be a critical enabler for aligning behind a common vision for data, helping increase organizational speed and agility, and providing consistently impactful and innovative insights.