Business Intelligence and Data

In my previous post I touched upon the barriers to transformation that were caused by data, essentially, silo’ed data and disparate business intelligence (BI) tools. In that blog I touched on the benefits of moving to a single platform to standardise data formats and utilising a single business intelligence tool.

In this blog I want to talk more about the benefits of using data across Local Authorities (LA) (or in fact any business) to move towards a data driven decision making authority and how to take some steps towards moving to this model.  

LA’s traditionally hold vast amounts of data, spread across multiple systems with little or no integration. What this means is that decisions are generally based on the data held in a single system. To put this into context an LA could hold data in three different systems relating to a single household. These could be a housing system, a social care system and an environmental system (e.g noise complaint). In today’s world, the LA would typically send three officers individually to the property, all three would visit and return to the office and input data into their own system offering no insight to the other services. With a common platform and single BI tool the LA could look across all systems and actually work out the underlying issue and resolve that, which may reduce the demand on the other services. The are also a number of examples I could give regarding finance where residents are requesting support in one area and being summonsed in another area with no visibility across the services.  

The benefits of joining up this data across a LA a taking a view of households (I’m a firm believer that we should be looking at a single view of a household rather than a single view of a resident) can be endless and will ultimately lead to the authority being able to make better data driven decisions.

However, in order to start to utilise this data better there needs to be a fundamental change in the way that LA’s currently operate. Taking a single view across households requires a corporate performance / data analytic team who need to be free to do what their job spec says - analyse data, rather that silo'ed reporting teams! Too many 'performance teams' are little more than a team who churn out report after report with little or no analysis of what the report says, and to make matters worse these reports are always backward looking and generally out of date by the time they are shared.

For instance, what use is an HR report telling you how many staff were sick last week. As an overall measure of the council it may have some use, but for the service itself it’s pointless. What the service needs is a report that day that states how many staff are sick on leave or absent and for an analyst to tell them the impact of this (based on historic trends) with regards to how the number of absences will impact performance or may lead to backlogs and therefore what decisions the council need to take that day - perhaps some additional temp resource to keep caseloads within tolerance.

So in order to change most performance teams will need to create capacity to move towards this approach. This requires senior staff to accept that the standard reporting solutions (predominantly spreadsheets) will need to stop and service managers will need to self serve via the use of pre-built dashboards. From a service manager perspective, they don’t need the full view across the authority, they just need to understand how their own service is performing.

Simple dashboards with today’s BI tools are very easy and quick to create and service managers will be able to view their data with very little training. A secondary benefit of today’s BI tools is that they require very little ICT investment or overhead. Traditional BI tools used in LA’s normally require a specialist skill set and a team of people to build, manage and maintain both the reports and the environment and often have single points of failure (people).

If service managers can then begin to self serve then an LA can remove those daily spreadsheet reports from being sent out and the corporate performance team can begin to create the time needed to actually analyse data instead of just creating or replicating it.

Corporately, by standardising data formats and the BI tools used to interrogate it, the LA can move towards a data centric delivery model. Corporate dashboards can give a high level view of how the LA is performing that can be used to inform conversations and decisions in SMT meetings. A dashboard created for the Chief Executive will invariably show data across all services at a high level. Let's then say that two of the items on the dashboard are financial kpi's then you already have the first two items of the Finance Director’s dashboard, who then may want a further 4-6 items breaking that information down perhaps by directorate or service  (after all they will want to know if an element of the dashboard is about to go red before the CE does) and so on to Heads of Service.

In creating a ‘tree’ of dashboards the LA can begin work on a top down decision making structure rather than starting with trying to build a data warehouse and working upwards. It’s important though, especially in the early stages not to obsess with ensuring that all data and dashboards are perfect. There will always be an element of trial and error. The 6-8 KPi’s on the CE’s dashboard may change over time or you simply may not have access to the right data sets initially, but some of this exercise is around thinking and doing differently and therefore if the dashboard is ultimately showing the wrong data then it can be changed, but you have to start somewhere.  Another benefit of the dashboard approach is that it puts the emphasis of data quality back on the staff and not the performance team, who often spend as long cleansing data as they do creating the report. With data being very visible it’s easy to see where tasks aren’t being completed properly or on time.

The reason for this is simple - today’s BI tools are designed around data visualisation. It’s around showing you your data in a format that is very quick and easy to see issues. By spotting anomalies in the data in real time it’s then easier to take action earlier in the cycle than if you were to wait for a weekly report to be sent out, especially if it’s sent out in spreadsheet format where you might not even spot it. This may be as simple as a case officer not completing the necessary tasks to close a case or may be an actual issue. Either way, by seeing this immediately the council can take action, and in the case of the case officer not taking the right steps to close a case, can be amended and thereby the data is being cleansed by the service and as they go, not in one large exercise before a report is due.

A good example of the benefits of data visualisation can be seen in a number of exercises I have undertaken with various LA’s. This involves taking data that has been published on their website either through open data or as part of an FOI. In general this data is produced and published because it has to be and often isn’t looked at apart from to check it is right before being published (often only by Comms in case there is something controversial in the data).

However, by taking this data and uploading it into a BI tool I can very quickly start to break the data down and begin to analyse it. I can quickly go down dead ends and then take a different path without making any changes to the data. What this ultimately means is that I can take a data set from an LA that I’ve not worked with previously and come up with a series of visualisations and questions (normally 15-20) about what the data is telling me about that service that require follow up and I’m able to challenge the way that elements of the service are delivered.  The smallest data set I’ve done this with is 10 rows and 5 columns of data and still created 15-20 visualisations and questions. The data was actually published as two separate spreadsheets (each with only 5 rows).  This is a useful exercise for all performance teams to undertake. It starts teams to look at the anomalies in the data (often the highest or lowest bar in a graph) and begin to question the why’s and what can we do differently.

Looking further into the future and in a Utopian society then not only can the LA start to join up LA data that it holds but it can then look at external sources too. This may be data form within the household such as smart meter data, weather data, energy performance certificate data, and with the right permissions it could even be food shopping data or lifestyle data provided by activity monitors. What this can do I really start to enrich the amount of information that a LA can look at when viewing a single household and can inform a wide range of organisations on top of the LA such as Public Health and the NHS, maybe the Police with Crime data also mapped. The options going forwards are positively endless with the right creative mindset and skills. This then also leads into the world of predictive analysis whereby you can start to trend data and thereby plan better decisions into the future based on hard data and not anecdotes (i.e. crime goes up when it’s hot!).

I could write another 10,000 words on data but hopefully that gives a few ideas to people looking at where to start and how they can use data better to make decisions. Have a play with some BI tools, load some spreadsheets in and see what you can interpret and don’t wait until the data is perfect, trial and error is the way to go. If the performance team can show value then moving forwards becomes much simpler and the value of the team rises! And again start to think about how moving to a single platform enables easy access to all that rich data you hold. 

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