In this blog, Ritu Mahandru, River’s Managing Director, talks about performance data management in business and how to plot your business on the data and collaboration maturity curve.
Connecting mountains of data, switching in and out of complex systems and delivering reports to the board is a big headache for today’s businesses. So much of this is still done in silos and without collaboration from the right people. And business leaders still don’t have rock solid confidence in the role of data in their organisations or a clear understanding of where they are on their data and collaboration maturity journey. So, what are the steps towards data maturity and what are the hard productivity benefits of data collaboration for today’s modern businesses?
Too much to handle
Most leaders appreciate that sharing business data creates value, provides insights and helps them grow. That’s fine in principle, but often there’s just too much data to handle effectively. It’s also usually stored in multiple places with different data sources which means you really don’t know where to start. And then there’s the question of how fresh the data is in the first place. It’s little wonder that data is an Achilles heel for so many companies.
In most scenarios, you’ll have multiple people all taking their own inferences from the data. So. that might be the same emailed report, for example, but without the context or visibility of previous conversations when the next data update arrives. Sound familiar?
With that in mind, where do business leaders go to get an overall, consolidated view of how well their business is doing? And if we can’t get this part right, how are we going to shift to sharing and collaborating around data?
Data – a continuous pain in the head
Why is data such a big headache for businesses? Firstly, the data that is available is often not very usable, hard to get hold of and – quite frankly – flat. As a business leader, you want to drill down to see how different parts of the business are performing. Right? But how possible is that for you currently? At a guess, it’s probably pretty painful. Then there’s a dangerous dependency on data analysts. We’ve made data so complex and inaccessible that we need specialist teams to prepare it and produce insights from it. Often, the quality of data these analysts have available is poor, and then it’s out of data as soon as their interpretation of the data is shared with anyone that can really make decisions with it.
Secondly, there’s the challenge of organisational silos. In a culture of unfettered silos, teams will set up barriers to protect their area, and this makes it incredibly hard to encourage data sharing. If we blend these two elements together – a dependency on analysts and overly protective teams – it’s beginning to sound very much like a command and control organisation. Unfortunately, businesses that use KPIs that are only relevant to a specific team or small business segment, will lack a full picture view and will find collaboration only happens in small clusters – as opposed to cross functional teams. That might sound over dramatic, but the point is that the role of data is so much more powerful when it’s shared, socialised, and in everyone’s hands – instead of using it to maintain authority.
One final point to make on why data in business is such a problem, is conflicting goals. So, there might be data, and it could be up to date, but the things you’re asking the business to measure are at odds with each other. For example, a team may be required to improve customer satisfaction and at the same time, charged with reducing costs. These two measures are likely to be in conflict. It must be frustrating not be able to have a conversation around the data so teams can agree the measures that actually matter.
The market forces around data
To put this into context, let’s examine some of the external factors that are making data a more complex issue for businesses to navigate.
For starters, we operate in a global marketplace. Most businesses are now global enterprises or at least have a bigger network of suppliers which might stretch across trading regions. By nature, this creates a greater expectation of data availability. We need data to assess which markets are viable and which suppliers are most effective. As a result, businesses also have more systems to contend with – and these modern systems are better at producing data. SaaS products generally provide an API by default which allows for data from myriad sources.
At the summit of the data maturity curve, there’s also the emergence of Artificial Intelligence and machine learning capabilities. These big data possibilities provide predictive data which brings with it a whole host of opportunities for businesses that can handle that level of information.
When we pair all this with tighter budgets, we’re faced with a greater need to justify where our investments are working the hardest. These are the sort of pertinent questions that board reports now require.
Today, the face of business is changing more rapidly with a need to respond to visible data faster, and in a more proactive (or agile) way. As business leaders, we need to be able to anticipate change, and therefore, our reporting needs to be more frequent and up to date, so that we have constant visibility of what’s going on.
As a general rule, our leadership style is less hierarchical than it was a few decades ago, with more empowerment for our teams, which creates accountability. Self organising teams now need to support their activities with data. But how can we make this easier, so they don’t have to deal with twenty different system just to see how they’re performing as a team?
Data & collaboration – a maturity curve
According to a Harvard Business Review report on data blending, 92% of businesses are primarily using spreadsheets for their data analysis. That’s mind blowing, especially when you consider the vast number of data visualisation tools available to businesses today. It shows that no matter how mature we think we are, we always revert to what we know best and feel comfortable with – at the expense of being productive, innovative and game changing. And of course, Excel is easy to share in email and everyone knows how to discuss over email. So, we’re trying to achieve collaboration around data using rudimentary tools.
River’s data collaboration maturity model is based on gathering real world experience from our base of UK blue-chip customers on how they currently share and discuss their key performance data.
Level 1: Spreadsheets in email
Level 1 on our data and collaboration maturity curve, is where a business has a dependence on spreadsheets within individual teams. In this scenario, the easiest way to share data is to email the spreadsheet to fellow team members. It’s where data is out of date and not aligned with the rest of the business.
In this situation, confusion reigns. No one is confident in the data that’s available and suboptimisation is supreme. This is where people possess an unhealthy interest in the success of their team – and their team only.
Level 2: Complex spreadsheets
At the next level, there’s more business intelligence. This means that data is probably being pulled together from a number of sources and collated into one spreadsheet. So, it’s more meaningful and relevant, but the tools and spreadsheets are specific to siloed areas. It’s an improvement – more data is available – but it’s still not consolidated across teams and still not timely or accurate. And, of course, if it’s still being shared over email, the same problems persist.
Level 3: Online data dashboards
At this point, data is being consolidated across functional teams so the silos are breaking down. Teams can now have better visibility, which is great. Data dashboards and extraction tools are being used to make the data more accessible, but it’s not liberated across the business. It is also still likely that your level of seniority dictates what data you see or whether you see any at all.
So, there’s better data insight but it’s without collaboration. Email is still being used to share the data and discuss where the focus needs to be and where should teams be concentrating effort. The historical narrative is also lost regarding what the business did the last time it hit this problem.
Level 4: Interactive data collaboration
This is the space we want to see the majority of businesses. This level is all about collaboration around meaningful, simple data from multiple sources. Relevant data is collated at timely intervals, presented in a simple visual way to allow decisions to be made by teams. Teams are encouraged to focus actively on fewer metrics rather than trying to fix everything. Teams are truly collaborating across functions to move the needle on a metric.
The key to collaboration is getting teams using the data to create goals and activities, that improve business performance. That means they are empowered to discuss the data, share their experiences and offer historical perspectives. This provides an opportunity to really focus on how they can contribute to improving or maintaining the key KPIs that matters – within a particular timeframe.
Level 5: Human and machine learning
This level is really the nirvana of data and collaboration. It’s where there is a sophisticated use of Artificial Intelligence, machine learning and predictive analytics. And the business is using it to make critical decisions which inform their strategy and initiatives.
At this stage the business is able to anticipate upcoming challenges and mobilise teams to ensure they are well placed to weather any upcoming storms.
We work with our customers to realistically assess where they are on the data and collaboration maturity curve. We’ve found that most organisations are between Levels 2 & 3, and would like to be at Level 4.
Using River, businesses can plot a journey towards Level 4, and have a strong partner to take them to Level 5 (and beyond). Are you ready to future proof data and collaboration in your business? Get started today with a free trial.