Reporting and Business Intelligence (BI) are both incredibly vital tools for your business and each has a strong suit. In this article, we explain the difference between the two, in what contexts they are both respectively useful and how to ensure the data underpinning them both is of a high and reliable standard.
Reporting is usually thought of as being static. This means that it provides a snapshot in time of a certain area of the business, and is usually used to elicit a short-term action.
Reports are generated from data sets and provide a quick summary of the dataset, from which an action can be taken. An example of this would be a report of the orders requiring shipping for the specific period, which then has the knock-on effect of initiating the timely response to ship said orders. On a day-to-day basis, reporting can keep things operating like clockwork.
Business intelligence or BI is a more dynamic concept where it looks at past data to inform strategic decisions for the future. It does this by using a host of BI tools to analyse the intricate relationships between different data sets, that may well be pooled together into the BI software from the CRM or ERP software.
Taking the above shipping example, BI would provide information on the company’s shipping performance over time and in detail, for example, which carriers were more efficient, which warehouses outperformed and which products were more tightly controlled from order to shipping. This allows the company to make adjustments to improve business performance and ensure they remain competitive.
Understanding where one ends and the other begins
Although reporting and BI may well sound very similar, one cannot do the job of the other
It would be extremely unwise to use a reporting system to compare subsets of data from different sources. Doing so would require datasets to be manually pooled and compared in a spreadsheet which introduces a high risk of error and degrades the integrity of the data and final output.
Conversely, the BI tools are designed to automatically compare and analyse data in such a way that is reliable and reproducible and can provide information from a multitude of facets. One way to think of it that reporting provides a snapshot whereas BI forms a crystal ball, allowing understanding of the future.
Another key feature of business intelligence is that because of its accurate analyses of past occurrences, it can provide an accurate platform on which the company can establish KPIs going forward, to further improve their operations, customer satisfaction and turnover.
The cornerstone: clean data
And of course, the cornerstone to both BI and reporting is data. If the data on which these reports are based and analysed is flawed in any way, the result will be inaccurate and could fuel improper and inappropriate business decisions. So, how do you ensure data is accurate and can be trusted? You clean it. Read on for steps to take to clean bad data.
1. Get rid of duplicates
Duplicated data happens because two different systems have contained common data and are subsequently merged, or because data has been entered again in error.
Get rid of and avoid duplicates to avoid skewed results. BI tools help you automatically analyse for and remove duplicates, but in manual entering, it is important to always search for the data you are entering first so that you can refrain from continuing if it already exists. It is much easier to catch this at the start than to have to ascertain and correct it later. The same goes for merging data from two different systems or instances. The data with the most integrity should be retained while duplicated and older data is removed.
For example, with stocktaking, the data obtained from the stocktake would be used to replace the existing product’s data in the database so it is updated accurately rather than replicated.
2. Embrace automation
Although we have talked about spreadsheets and manual entry in this article, it is preferred to avoid this and utilise inventory management software that is designed to obtain, capture and maintain data in an accurate manner.
3. Systems to support data cleansing
Part of compiling good data and keeping it ‘clean’ is having the systems to support data monitoring and maintenance.
For example, this would involve systematic stocktakes that sample all areas of the inventory. Another method is ensuring there are regular audits that also consider and sample data in all parts of the company, looking for the aforementioned areas for error and ensuring that data collection and maintenance processes are working effectively.
With the rate of business development in the current global manufacturing world, large volumes of data are generated every day by a company’s operations and should be considered and valued for the insight they provide. However, the company decisions are only as trustworthy as the data from which they are made, and that is why it is so important to maintain ‘clean data’. If you are struggling with erroneous data in your manufacturing and inventory-based business and want to get things ship-shape, do get in touch today!
Article by Melanie Chan in collaboration with our team of Unleashed Software inventory and business specialists. Melanie has been writing about inventory management for the past three years. When not writing about inventory management, you can find her eating her way through Auckland.