How to Build a Watertight Business Case for Your Data Analytics Project



SSON Editor
03/21/2016

Organizational development lies at the heart of any successful modern enterprise, many of which are currently reviewing their modus operandi to lead the charge towards more effective global performance. A crucial "HR" component is the ability to gain access to real-time data on important information like the global employee base. For Finance, it’s about correlating data from customers with data from processes. For Procurement it’s about transparency and insights on the supply chain.

Wherever you look within the enterprise, ‘data’ is emerging as the secret sauce to performance. However, while no one denies the significance of data analytics, most people are still stumbling over how to stand up a team and where to start.

Reasons for hesitating include lack of resources to allocate to the project, lack of prioritization, perceived lack of technology needed, insufficient skill sets on hand, and lack of ownership.

The truth, of course is generally simpler than we think. For one, data analytics is nothing more then a closer, better, more regular look at the data already flowing within your systems. At least that's where it starts for most organizations. In addition, while data analytics conjures up expensive technology investments, the process and behaviors can be implemented with what you already have. And finally as with so many things, the analytics alone don't solve a thing; you need to communicate the story to the decision-makers that count.

Getting to ‘one version of the truth’ means far more effective deployment of international human resources for enterprises that are struggling to work out a global growth strategy based on making the right decisions around talent. It also means fewer mistakes, more reliability, and better financial data to base decisions on.

Here is how you set up a business case for data analytics:

1. First and foremost, you have to engage your employees for the new and exciting world of data analytics. It's not about new titles or new solutions – it's about recognizing the value of the information flowing across their desks (okay, across their screens) and taking a different, more engaging approach. If you open your employee's eyes to the endless possibilities of analytics, you will find useful ideas emerging at every stage. That is the foundation of a data analytics program.

2. At the early stage, process mapping is important to ensure that the initiative fits the whole world, not just individual markets. So be clear about the processes and be clear about the data they contain.

3. To lead an effective initiative, the project manager needs to own all the relevant data, processes, technology and strategy – on a global level. So make sure you have the necessary clearances and authorities in place.

4. Even if you're not starting with a data and analytics capability, delivering greater efficiency across the enterprise relies heavily on easy access to real-time data, and trusting ‘one source of the truth’ at its core. So accessibility is key.

5. One of the biggest hurdles to standardization and reliable business intelligence is "multiple sources of information". The ideal end state will hinge on one point of access and one channel. Even where this is overly optimistic, it's important to keep it in focus. Local workarounds, shadowing, sticking to old ways of doing things instead of committing to new… all of these will throw a spanner in the works.

6. The big differentiator, when it comes to a data analytics environment, is that you are able to manipulate your data faster, and trust its validity. For a business that needs to react quickly to changing internal as well as external factors, where agility is valued as a survival mechanism, robust data analytics will be key.

7. The implication of making the right resource and personnel decisions is huge. Today's data-leveraged business environments require different skill sets. But that is a lever, not a cost. Think only of what else ‘intelligence-driven’ staff can do for the enterprise. These people are trained to recognize trends and patterns – some of which may not be on anyone's radar as of yet. Opportunities are endless.

8. Technology is not a hurdle – it's a facilitator. Again, whereas many practitioners assume data analytics requires an entirely new set of solutions, in truth much can be done with the old spreadsheet. It's just how and where it's mined, how and where it's reported, and the implications and predictions you draw from this reporting that are different. Which brings us back to the first point: data analytics is as much a cultural mind set as anything else.

9. Smarter decision-making. It's the Holy Grail of support services, and it’s been given an enormous boost by the promise of data analytics. While reporting last month figures is increasingly redundant, real-time data – yesterday’s or today’s – is fuelling shared services value-add capabilities. With improved data analytics, you gain the ability not just to predict next month or year, but also to prescribe appropriate reactions on the part of the business to pre-empt challenging situations. This, more than anything else, defines data analytics’ value. Operating in new markets, with new customers and suppliers, the importance of basing your decisions on reliable data is paramount.

10. At the end, of course, it comes down to how well data analytics supports your enterprise objectives, be these in terms of a diversity strategy, a location decision, weeding out a vast supplier base, identifying root cause of errors, highlighting "problem" customers… The list goes on. Whatever will be additional and peripheral value add, you’ll need to demonstrate first and foremost that time invested in data analytics will pay off by helping your customers with their day-to-day challenges. From there you can grow to anywhere.

One thing that is certain...

Every Shared Services will be called upon to make more intelligent decisions going forwards, and be more intelligent about its operations. A culture will lead the way, technology will enable it, but its the people, up skilled in the language of data analytics, that will make the difference.

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