Is this whole ‘data thing’ overhyped?.
We live in the age of Data! “Really?”. “Yes!, Data!, tell me more!”. Every small restaurant or plumbing company is working on its ETL processes, AI and machine learning, and you?
Data has grown into one of the major buzzwords of our recent times. Simple Excel clerks are now being called data annalists, or worse: data scientists. What we use to call statistics is now being called machine learning and everybody seems busy with it. It is easy to think of data as the next big hype and neglect its use. However, also for smaller businesses data is a really important subject. So, lets step over all the overhyped fuzz and focus on what is really in it for small or medium enterprises!
Data for informed decision making
The basic dynamic of running a business is investing in profitable assets and divesting in non performing assets. Sometimes we need to invest more time and effort in that department that is yielding decent returns instead of that dated line of business in which we once started our company. Taking these type of decisions requires information. With the right tools and infrastructure we can have a dashboard that help you answer the questions that underpin these important decisions. Questions as:
- Is this customer actually profitable?
- How much profit does my consultancy department bring in and how much does our construction department generate?
- What are the most important costs drivers in my overhead spending?
Often accounting software has only limited reporting possibilities and is not linked with other data sources such as visitor counts of websites or production data. In popular data tools such as Microsoft Power BI we can link these data sources and combine this data in a dashboard that can easily be filtered among dimensions that are relevant to our decisions. For example: profit filtered by project or customer, expenses filtered by department, or the ratio of profit relative to time spend by employees. You ask the question, we get a chart or number that answers this question.
Data for a glimpse into the future
With a large set of (historical) data you can predict and anticipate harmful events such as inventory shortages or production outages. Examples of these are data used from a series of sound and vibration sensors to predict a machine failure. Or a a build-up of customer interest measured by increases in store visits and webshop interactions that lead to a burst in sales. These type of data applications require more sophisticated statistical analyses ‘machine learning applications’ and a longer period of consistent data collection. These type of data solutions might be useful for some small businesses but it is good to be critical about the amount of costs involved vs the benefits of these type of data applications. Ofen scale is a requirement to benefit from these type of data exercises.
Big scale cloud solutions such as azure data services or amazon-EWS are often not that useful for medium or small businesses. Neither are machine learning algorithm or AI. the advantages of data solutions for smaller companies predominantly lie in more descriptive analyses facilitated by Microsoft Power BI. Luckily, these types of data solutions are also among the most straightforward, making them easily deployable on simple and basic IT infrastructure.