| July 8, 2019
There is a lot of hype around the idea of data analysis - phrases such as ‘Big Data’, ‘AI’, ‘Deep Learning’ and all the rest mean that small businesses tend to see formal data analysis as the preserve of larger businesses. This would be the wrong conclusion to draw as the competitive landscape for ALL businesses, large and small, gets tougher every year.
Whether its called Business Intelligence (BI), Analytics or plain old “Management Information”, it all comes back to the same thing….. using internally generated data can greatly help even the smallest business make better decisions and improve performance.
What is the objective?
This is an important question to get right, so here is a starter for 10…..
to develop a process for making business decisions that are consistent, repeatable and lead to improved results for the business
You may come up with something more nuanced, or completely different. However, it is important that this question is answered before taking the next step. One solution is to think that about the reasons for doing data analysis in the following terms:
- Description - what is our current state and what happened to get us to this point?
- Prediction - what might (perhaps, will?) happen given the current state and certain circumstances?
- Prescription - what action is needed to improve or correct issues that arise from the above?
In addition, the decision to improve data analysis should be seen as an extension of existing practices - don’t dive straight in, expecting a smart new software tool to automatically identify the right questions to ask, and then provide answers to those questions, without thinking things through.
It should also be stressed that improving data analysis will be a journey, not a destination, so starting with a plan will lay the foundations for developing the necessary data and analytical infrastructure. As with any new project it is important to be “analytical” in the approach taken:
- clearly define the business problem that needs addressing - is it a cash issue, customer service metrics, stock availability?;
- identify data sources and where the gaps are - do you even collect the data you need? can you access it in a way that makes sense?;
- ensure the integrity of the data on which decisions are made - can we trust we’re getting accurate and consistent raw data from a verifiable source?;
- select the tools appropriate to the problem at hand - are we talking a one-off fact find or a ‘system’ that needs to be usable by non-technical users on an ongoing basis?
Getting Organised
Beware of ‘Patchwork’ Computing - pretty much every small business will be performing some level of analytics already - in all likelihood using Excel/LibreOffice spreadsheets. If you or your team are inputting data into spreadsheet tables, combining multiple spreadsheets and overlaying with other data from disparate sources, as well as performing complex v-lookups and pivot tables on a regular basis, it is time to get more organised.
Data Sources
Internal This sort of data should ‘fall out’ of the day to day business process….. from the ‘ERP’ system (if one exists), data collected from the company website via Google Analytics or generated in response to specific process flows (e.g. inventory movements, credit control activity or payment processing).
By assessing each of these sources it becomes easier to formulate a plan that should start with some simpler, low hanging fruit and progresses to more complex requirements. One approach is to begin by focusing on a single functional area (perhaps inventory management) and generate the data to answer the question “what happened?” and “where are we?”. If the data is not readily available, then steps can be taken to implement systems that collect it during normal workflow, rather than having to (for example) set up a special one-off collection methodology by inputting into a spreadsheet.
External Social media, geo-spatial and weather data, plus insights from paid sources such as Nielsen and D&B are also useful when ‘blended’ with internally generated data.
Software
Just as advances in technology have made data more accessible to your business, so too have they developed easier and more economical ways to manage it.
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Spreadsheets - the tool of choice for the ‘Citizen Programmer’ and the bane of the Corporate Auditor. Spreadsheets have their place in the world for ‘quick and dirty analysis’, or to bootstrap a new analytics process, but they shouldn’t be relied on for day to day data collection and regular ‘Description’ type analysis;
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Report writers - if there is an Accounting/ERP system in place the chances are it will have a report writer. These are usually a good start to get information to key decision makers based on actual, verifiable transactions.
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Dashboard software - the ‘silver bullet’ in many people’s minds. They do require an accurate and up to date data feed to make them useful which means that getting to this point in the journey can take time. However once there, well designed dashboards in key areas provide a fantastic ‘call to action’ for operational personnel - open source software in this area now means that there are low cost options for SMEs not available previously.
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Browser-based services - these can make it easy to enter, manage, extract, combine and analyze data with the power of a database and the ease of use of a spreadsheet. As with dashboards there are open source solutions that can be implemented by SMEs at reasonable cost to put very sophisticated analytical power in the hands of key users.
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Analytics software - the best (and most expensive) systems draw information directly from ERP systems, web front-ends and external sources to provide great visualizations, then migrate to a self-service analytics platform (possibly in the same tool or one that integrates with the visualization platform) to address more complicated, forward-looking questions.
Closing Thoughts
As the introduction states - even smaller businesses should regard their internally generated data as key resource that needs to be used, like any other business asset, to improve business performance.
Our approach to SME business analytics at uzERP would be to use the framework above:
Descriptive -> Predictive -> Prescriptive
In the first instance this would involve generating reports from uzERP or any other existing Accounting/ERP system, or building a fairly simple dashboard to highlight performance through analysing transaction data. Hopefully this would tell management something they don’t already know - or at least were unsure of or could not prove.
From there, we would progress to developing some drill down capabilities into the reports/dashboards to provide detailed insights into performance issues - this could also involve making the information more widely available so as to spread the right culture broadly throughout the company.
A more sophisticated analyses, that blends different data sources (internal and external), in order to answer specific business questions and provide real-time insight into performance might be the next step - a cashflow forecast is a perfect example of this type of analysis, being based on internal information (upcoming payables, receivables) with forecast delivery patterns and forward currency rates.
Finally, we might move on to more advanced predictive analyses and data sources (e.g. external market data) and incorporate analytic results into the business processes to ensure actions are taken based on the analysis produced. A good example would be a sales forecast model that combines internal sales/quote conversion history with information from field sales personnel via a CRM system, and external data sources such as a customer ’extranet’ or Nielsen Market data. This forecast data could then be used as an input to a capacity planning system to plan forward loading in manufacturing/logistics, improving on time delivery.
And finally…. if you want to know more about how we can help you improve your business through better systems and improved information then do please get in touch via the Contacts Page.