7 Steps to Launching Data Analytics
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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 component is the ability to gain easy access to real-time, granular data on important information, like the global employee base or suppliers. The ability to correlate data from customers with data from processes, and improve transparency, is the key to successful service support. Wherever you look within the enterprise, "data" is emerging as the secret sauce – and the ability to turn this data into insights is what everyone is after.
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. Hurdles include lack of resources to allocate to the project, lack of prioritization, perceived lack of technology needed, lack of skill sets on hand, and lack of "initiative 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. 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.
"At Vonage a self-service analytics culture has been a key success factor to promote our Enterprise Data Warehousing [EDW] efforts," explains Tom McCammon, Senior Director Enterprise Datawarehousing and Analytics. "Growing the data analytic skillset in the business teams has proven to be quite fruitful. The analysts are closer to the data than the Shared Services team and are able to understand, correlate and translate the outputs with real business impact – leaving the Shared Services EDW team to stay focused on ensuring that the single source of truth is as accurate as possible."
Getting to one version of the truth means far more effective deployment of resources for enterprises that are struggling to work out a global growth strategy. It also means fewer mistakes, greater reliability, and better data to base decisions on.
Here is how you set yourself up for a data analytics program, and some key benefits that will support the business case:
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.
"At Vonage we have created a ‘Power User’ forum where analysts can share their ideas and techniques," says McCammon. "This forum has members from the EDW team as well as elite data analysts from each line of business. It has not only helped raise the bar with alternative thinking – it has also created a stronger bond between the data analysts and data providers."
2. 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 and ensure whoever leads this initiative is clear on their scope.
3. One of the biggest hurdles to reliable business intelligence is "multiple sources of information".
The ideal end-state will hinge on one point of access, one channel, andeasy access to real-time data. So accessibility is key.
Miguel Portugal, Business Transformation Director for Information Management,LatAm, at SABMiller, concurs: "A single point of access is key, particularly for executives. The world of single data source is no longer valid. The speediness regarding how and where data resides, as well as the very demanding and changing requirements, have made us realize that the problem is not about the solution itself, but about the reality of the architecture."
4. Even where a data and analytics initiative seems overly optimistic, it's important to keep the end-objective 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. Eliminate hurdles one by one in support of coming together for a better way.
5. Technology is not a hurdle – it's a facilitator.
But while many 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 it that are different. Which brings us back to the first point: data analytics is as much a cultural mind set as anything else.
"At SABMiller we are re-using existing toolsets complemented with free software to bridge the gap and deliver really insightful analytics," says Portugal. "For piloting and proof of concept this is very useful. Industrialization, however, can be a little bit harder."
6. Smarter decision-making is the Holy Grail of support services, and it’s 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.
"The Vonage executive leadership team is committed to data analytics for smarter decision making," explains McCammon. "Our business leaders have recognized this and continue to promote the analytics culture across the organization by supporting the EDW Shared Services team and recognizing the business value that each of their data analysts bring to the table."
7. At the end, of course, it comes down to how well data analytics supports your enterprise objectives.
No matter whether these are 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 the additional 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.
"One of the key things we have realized at SABMiller is that analytics has to be embedded into the business management routines," adds Portugal. "So establishing communities with key business stakeholders and decision-makers is key to ensure proper discussion on insights, facilitated action-driven results, monitoring results and their effectiveness, and finally driving continuous improvement on the insights to close the virtual cycle."
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.
The big differentiator in a data analytics environment is that you are able to manipulate your data faster. 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.
One thing that is certain is that every Shared Services will be called upon to make more intelligent decisions going forwards, and be more intelligent about its operations. It’s people, up skilled in the language of data analytics, that will make the difference.
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