What Is Possible with Data Analytics

Smaller Businesses Can WIN

Hi there, and welcome to this post where I'll do my best to explain how small to medium sized organisations can profit from data analytics. The focus is on smaller organisations since there is already a lot of information out there about the use of data in large businesses. In fact, there is a general perception that data analytics is only for large organisations. However, this is completely false! Today every organisation either has actionable data or has the ability to collect it relatively easily. I will go further and say that data analytics presents a far larger opportunity for smaller organisations than it does for large organisations. There are two reasons for this:

• Large organisations have accumulated huge, disparate and unwieldy IT systems that must be integrated together if they are to provide analytical value. These organisations spend years, and millions upon millions of pounds, euros and dollars trying to integrate these internal systems. Often their attempts to integrate these systems fail.
• Effective data analytics requires that everybody in the organisation, from the very top to the very bottom, buys into the idea of using data to drive decision making. Well, larger firms are overwhelmingly likely to have middle managers with entrenched fiefdoms, who resent having to put their intuition and 'gut instinct' second to the data. Their status and power is founded on their so called 'expertise'. If the data knows better, then what does the company need them for? In this way, too, do data initiatives at large organisation fail.

So I put it to you that the prevailing perception of data analytics is completely backwards. It is smaller businesses who have the most to gain. In fact, data analytics represents a huge point of advantage over larger competitors. When it comes to data, scale has proven to be a hindrance.

The Two Types of Analyses

So let's get down to explaining how the smaller enterprise can use data to its advantage. The process of data analysis can be split into two parts: descriptive analysis using a BI (Business Intelligence) tool, and more complex analysis using statistical methods.

Once we have organised data (the process of getting our data organised is for another blog post) we can derive massive value simply by implementing a BI tool which will give us a coherent view over the data, customised to present precisely the metrics which are most important to us. Even better, it gives us the power to dive into the data and analyse it. We can now do data analysis!

Business Intelligence (BI) Tools

There are different types of BI tools:

• Full service tools (the most common type; full-featured but need IT people with coding knowledge to set up and maintain)
• Self service tools (less powerful but no coding is required; this is the type of tool we usually recommend)
• Visualisation tools (we can't drill down into the data - we merely get an intuitive and pretty visualisation of our chosen metrics; much less powerful than a full BI tool but still much, much better than no tool at all)

These are the tools we recommend for self-service and visualisation:

Self service

• Tableau Desktop
• Klipfolio
• Chartio

Visualisation

• Databox
• Grow.com

These tools all offer free trials and helpful guided demos so the thing to do is to try them out and see which one fits your organisation best.

Statistical Analyses

Wonderful, we now have a BI tool implemented and are a truly data driven organisation! We have joined the ranks of Google, Facebook and Amazon and the other data-driven organisations that are taking over the world. Congratulations! But wait, there is much, much more that we can do to improve almost all aspects of our business. There are techniques available to us which most people think of as rocket science, but which are actually decades old, and are mostly quite simple. I'm talking about the application of statistical methods to our data, which you will probably have heard of described under terms such as 'Machine Learning', 'Artificial Intelligence' and 'Data Science'. Despite the fancy names, all we're doing is using statistics to predict things, optimise things, or find connections between things.

These statistical methods have been around for a long time, some since the early 1800s! (see our friend Carl Friedrich Gauss above) However, while they have long had niche application, they have become much more widely applicable in the last 10 years due to three factors:

• the avalanche of digital data caused by the modern internet
• increases in commodity computing power
• reductions in the cost of computer storage

Now organisations of every size produce huge amounts of data, can afford to store it, and have access to the computing power needed to apply the old statistical methods to the data. They can use the results to

• acquire customers more cheaply
• keep existing customers for longer
• make existing customers more profitable
• improve the efficiency of internal business processes
• better understand customers and the market
• identify new opportunities for markets, products, cross-selling and up-selling
• optimise pricing
• predict demand
• and more!

YOU Can WIN Through Data Analytics

Phew! As you can see there is a lot that we can do. The opportunities are huge. As mentioned before, your larger competitors are most likely already trying to use their enormous troves of data to achieve all of the above results. But the opportunity is much greater for small and medium sized organisations. Their scale is not working against them. Their data systems are far simpler, and their internal culture is wonderfully non-political when compared to the Game of Thrones-type shenanigans taking place throughout almost all corporate hierarchies.

You as a leader in a smaller enterprise have the power to completely transform it into one that's truly data driven. If you're not already making visualisations of key metrics available to everybody in your team, then I urge you to consider using one of the relatively simple visualisation tools I mention above. If you perform no other data analytics, applying analytics to the customer acquisition process will provide you with massive benefits. Any individual process within your organisation is a candidate for optimisation.

The potential rewards are huge. You can increase revenues, reduce costs, defeat far larger competitors, and achieve laser-like clarity around all aspects of your business. All it takes are old techniques, new computers, and skilled people.