With the volumes of data available to organisations only getting more abundant and complex, it can seem harder than ever for businesses to get a grip on their data. No longer is prowess in SQL or great skill in advanced Excel use enough to tame the big data beast. Although knowledge in these will certainly remain crucial in the business analytics sphere, many can sympathise that combining reams of fact tables into complex queries can be an immense source of tedium for even the most practiced Excel gurus.
So those businesses that have made in-roads into the challenge already know a few things when it comes to the problems and some clever workarounds. But there are more effective and less effective ways to take the initial stages of data mastery into something truly transformational to the whole business.
That data mastery probably looks like the adoption of a repeatable, workflow-base self-service model. Analysts on the go don’t want to be begging IT’s help, or to waste insights-finding time with coding. With initial gains proven, and the new data mastery having an impact on time (there should be more of it, for a start!) teams should be ready to make a visible, impact on your business on a larger scale. What are some of the next big rocks you need to move up hill?
The length of time needed to find, prep, and blend data – before even getting to query and finding insights – can be staggering. This is where self-service data software brings some time and joy back into the lives of analysts. Rather than relying on legacy systems of static data warehouses, analysts wanting to work faster and smarter can use software to pull information from a wide variety of sources into one central workspace, whether it be Excel spreadsheets and XML files stored in a company’s data warehouse, huge data sets stored on Hadoop, or publicly available data sources from the Internet.
Once all the necessary information is present on one platform, rather than spend the majority of their time preparing their data, analysts can easily pick from their resources and instantly blend the relevant information together to create a workflow specific to the business problem at hand. It’s also an easy way to inject more enjoyment back into the roll.
For all the power and opportunity data prep tools present, many businesses still find it difficult to convert their data into strategic insights. It’s a common problem that can bedevil new projects, once the initial easy wins have been and gone. MIT scientist Kalyan Veeramachaneni has noted that among businesses he advises, many data scientists are hard pressed to generate value from their models. Due to the amount of time it takes to clean up, analyse, and process data, analysts end up having the tendency to spend time fine-tuning their models in the later stages. They end up tinkering with the technology, rather than looking at the bigger business picture.
To avoid getting bogged down in the details of data analysis, analysts must have a clear view of their company’s overarching business goals. Team managers should spell them out with numbers to make sure individuals are engaged with these objectives. Whether that is increasing revenues by a certain percentage, generating a particular number of leads or converting a certain amount of leads into sales, managers need to make clear to their teams what their measurable business objectives are. Once these goals are specified as numbers, the prospect of achieving them will seem more within reach. This is what separates the have-a-go analyst from the hard-core analyst.
In order to boost the effectiveness of data analysis, it is never a drawback for analysts and their team managers to be practical when crafting new solutions. There is a temptation is there to create complex insights by blending more and more data together, with a view to solving multiple business problems in one fell swoop. However, it can be much more effective to focus on a few simple models. These can be deployed more quickly and easily so that, in the long run, more problems are solved more efficiently, as opposed to a single, complicated model which might take longer to deploy since it’s harder to make ‘perfect’. Working with this mindset, not only do employees feel more rewarded once their insights are implemented, they also get time back for themselves.
Finally, businesses can maximise their analytics by making their insights flexible. Rather than working in siloes on their analysis, business groups should build relationships with each other over their shared data. For example, HR will need access to data managed by Facilities and vice versa that have to do with office space projections. In this way, they can assist each other on common business challenges, maintain consistent data governance practices, and ensure that the entire business is benefitting from the big data revolution. Employees are all included in the overall vision and working to overlapping business goals.
Organisations have a lot to gain from a culture of analytics, support by the right software/platforms and services that fit their style of business. However, employees must be mindful not to bury their head in the technology, the process, or the data.
Data analysis can only generate value when it is able to bring the people closer to their business goals. To do this, it is important to keep data analysis processes in-line with realistic business objectives at every stage, from start to finish, project scoping to insight delivery.