Intel Corporation explores the forces driving enterprise priorities for analytics strategy and goals.
When we speak to customers about delivering in analytics, we look to be clear on one thing: that it is challenging but worth it. There can be no shortcuts on the journey for many reasons, as we discuss in our blog Prepare Your Next Step to Analytics Greatness. For many organizations, the challenge appears insurmountable, and you may ask: how on earth can my organization expect to become ‘data-centric’? Don’t you understand how many challenges we already face? And how do I know where to start and which technologies to use?
These are good questions. A starting point for the answers is that it is clearly impossible to do everything at once, so decision-makers need to prioritize their actions. Enterprises can find they are walking a tightrope, as they consider how to respond to data’s multiple impacts:
- An evolving technology landscape. Digital technologies refuse to slow down, as innovations in every layer of the technology stack, from silicon to application software, create new architectures and business models. The Internet of Things is a recent manifestation of a technology environment that will not stop evolving.
- An explosion of data sources and capabilities. A consequence of this evolving landscape is that new data sources are coming online, as well as new ways of collating and aggregating data – for example linking vehicle location data with ticket purchases to help public transportation companies better manage their fleets and deliver a more timely and reliable service to customers.
- Growing user expectations. User demands are rapidly increasing from the ground up, as managers and staff have access to smartphones and tablets, with direct access to online, real-time data sources. Why is corporate data not accessible inside the organization, they ask, if public information is available so freely?
- Increased overall priority on data and analytics. As lines of business find themselves affected by data, the executive board is also increasingly likely to set data-related goals from the top down. For example, we see this across corporate strategy as articulated to external stakeholders through annual reports.
- Changing skills, competencies, and expertise. Universities tell their students that many skills they now teach are for jobs that do not yet exist. In the same way, organizations are finding out what technical, organizational and business knowledge they need to stay competitive in the data era.
Each of the above requires a balance to be struck between what is possible for the organization to do, what it can do in practice and what it wants to achieve – we can see this as a Venn diagram of data-driven business models and outcomes. No enterprise can be all things to all stakeholders, meaning that it needs to set priorities according to what is true for all three – that is, the triangle in the middle of the Venn diagram. Deciding what goes where is a collaborative exercise between the business and technology teams – we have seen workshops between different stakeholder groups that use this model deliver high levels of success in this area.
Having established what is possible, decision makers then need to decide the order in which they are going to proceed. In our eGuide Putting Analytics in the Driving Seat, we talk about the priority is to understand where an organization is on its journey – we have learned across the decades that few (if any) businesses can run before they can walk when it comes to analytics. The organization should see its first activities as a way of getting on the journey, rather than arriving at the ultimate destination.
So, for example, in retail, an organization may still be in the mode of weekly reporting rather than real-time reporting of store data. Of course, automated stock control may be an aspiration but the first goal can be to get more timely information into the hands of ordering staff, so they can work more efficiently. We often find that such a step generates additional benefits, for example, the data can also reveal where stock could be better placed in stores, or decision makers may be in a better position to request more specific, valuable reports once they are released from the burden of analyzing weekly data by eye.
Overall, the goal is to work on the basis of a multi-dimensional 80/20 rule, focusing on the activities that will generate the most value for the business. Often this can only be established in hindsight, which is why we advocate proof-of-concept (PoC) exercises before an organization goes all-in on delivering a specific analytics strategy. PoCs not only test ideas, but they bring stakeholder groups closer together and engender that all-important trust.
Yes, deciding on an analytics strategy may feel like walking a tightrope, and there are many potential routes it could take. By setting priorities collaboratively up front, then testing them with a PoC, your organization will be best equipped for analytics success.
Learn more about how advanced analytics can help you transform your business, and what you can do to make it happen, by reading this new eGuide from Intel.
Find more information on data-driven insights and advanced analytics by visiting our Turn Data Into Insight website, where you can find it all in one convenient location.