At a basic level, Business Intelligence (BI) is about gathering data and deriving insights from that data to better understand your business and make improvements.
In order to produce Business Intelligence, an organization needs a system and set of tools for our 4-step process of storing data, organizing data, analyzing data, and reporting insights.
In order to have data on the processes we’re trying to improve, we need somewhere to store that data.
Paper documents (time-clock punch cards, warehouse manifests and fulfillment docs, and manual ledgers) and spreadsheets have historically been used for the storing, organizing, analyzing, and reporting steps, and many organizations still depend on these basic tools for data sources for BI.
But many of these manual storage methods have been replaced by automated systems that store their data in digital format such as flat files or relational databases, such as Microsoft SQL Server, IBM DB2, Oracle Database, Amazon RDS, and Google’s Cloud SQL.
This means that modern BI tools, which either have native integrations to these data stores (in the case of relational databases such as Oracle or Microsoft SQL server) or have application programming interfaces (APIs) that support communication with databases, production machinery and devices, and any other automated system that produces data and also has an API, can be configured to pull the data needed to produce your BI.
And as Cloud-enabled databases and Cloud Computing platforms have become more available, data can be pulled from multiple Cloud-connected solutions and Internet-of-Things-enabled systems to enable more informed business decision-making. (Eric Knorr, Editor-in-Chief at InfoWorld, has written a solid piece on “What is Cloud Computing”, describing the major pieces and the major players.)
Once the data is collected and organized – data pulled from disparate systems needs to be logically organized so that inventory data bit A is connected to sale data bit B – the next step is analysis.
What does all of this data tell us?
This too used to be a mostly manual process, but with the ability to store, access, and analyze large amounts of data (“Big Data”) using relatively low-cost Cloud Computing platforms (relative to having to own and maintain disk farms and server farms), we’re now able to rely on powerful BI software tools to do the more labor-intensive tasks such as predictive data analysis.
Some of the more popular BI tools are:
Again, these tools, which all have Cloud-based versions, enable you to pull data from multiple data sources, identify the relationships amongst the data sets, and perform automated analysis on the joined data.
This type of BI functionality, along with the multiple types of visualizations that can be generated for reports and dashboards, give data analysts the information they need to be able to understand and communicate to management what the business data in question means.
That said, “Business Intelligence Process”, if you will, has to include an actionable Business Process Improvement plan that can be implemented, and it’s impacts measured.
As we mentioned above, we collect and store business data so that we can, among other things, make improvements to our business processes.
The “how-to” of creating a Business Process Improvement plan is outside of the scope of this blog (if you need help there, we can help), but suffice it to say that the plan needs to have measurable outcomes that can be baselined with data we collect and that can be measured and can be measured at some future point against our baseline to determine the for effectives, positive or negative of our implemented changes.
Here again, our BI software comes into play as we use it to collect new data, compare it against our historical data, and glean insights regarding the effectiveness of the changes we’ve implemented.
At the beginning of this blog we described Business Intelligence as the product of gathering, organizing and analyzing data, and reporting insights gained from the analysis, with an eye toward taking steps to make improvements and measuring the results of those steps.
On the face of it, those 4 steps seem fairly straightforward.
But getting every step right is critical and requires both technical and business acumen.
The technical acumen required is around things like:
The business acumen starts with understanding the industry, the business function within the industry, and the desired outcomes on the front end, and being able to create a process improvement plan, execute on that plan, and measure the outcomes.
Having the technical expertise without the Business Process Improvement expertise results in insight but no ability to make improvements.
At Tridius, we’ve worked helped clients in industries from Oil and Gas to Healthcare identify gaps, design process improvement plans, and execute on those plans to boost the corporate bottom line.
We have both the technical expertise and the business acumen to help your company use its business data to generate actionable BI to improve your business.
If you have questions about how to make BI work for you, or if you need help on a specific BI project, contact us today and let’s talk about it.
Tridius Technologies (a Terralogic Company) is an IT consulting firm that blends strategic planning with technology innovation, in areas like Cloud Infrastructure, Application Development,
Managed Services and Dynamics 365. Our mission is to inspire, innovate and improve.
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