Today we are talking about adopting business analytics and what we have found to be best practices for their successful adoption and use inside the enterprise. The first observation is that we noticed that organizations that outsource their IT activity, I can’t remember if it’s IT specifically related to business intelligence products, but organizations that outsource their IT activity, depend on external consultants rather than develop the position internally, they tend to be less mature and get less benefit out of the IT solution.
The theory is that those external resources don’t know the business as well as internal resources. The second point to make is that sometimes organizations are not ready to adopt business analytics best practices. They realize they have to make improvements but they are not ready. It relates largely to business justification.
Often we have seen IT organizations driving the BI project, and they just don’t understand it well. They think about, wow, we have got to get the latest business intelligence product up and running. They are not thinking about what it means for the business. Or maybe they already have a dashboarding product but they want a better one. That’s not enough of a business justification. Those organizations who don’t have a dashboarding product need to be shown the business value of having a dashboard capability. There is an opportunity to get more business justification.
| #1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index |
|
Read More |
Don't Just Focus on the BI Technology
When we talk about business analytics, we don’t focus on just the technology. It is less about whether you are using reporting or dashboarding or OLAP. It's more about what are the types of analyses you need to be able to do, and what are you trying to accomplish with your analyses. In other words what are the types of metrics that people are using, and what they are trying to accomplish by using metrics? Whether they are displaying them on a dashboard or whether they are displaying them in reports is not as important. So at first, it’s not what mechanism they are using for these analytics, but this is more about the types of analytics that they do.
I think most organizations are using key performance metrics. They may not actually be doing very sophisticated metrics. But one thing we find is that they are extremely slow in delivering the analytics that they do produce. So timeliness is an important point. They need to speed up the process. If they don’t change the analytics that they are doing, and they can deliver them in a more timely fashion, they would get more value from the BI solution.
Now, this is about the technology that can be used to perform business analytics. This list gives you an indication of some of the types of analytics. Advanced analytics like predictive analytics are sometimes used. The other thing to take away is that spreadsheets are still used as the most common BI tool, and that’s really across business segments. There are a couple of segments where business intelligence replaced the spreadsheet. But spreadsheets are #1 in most of the segments and in a couple of segments, they are #2. But clearly, people are still relying on spreadsheets.
So when it comes to building your dashboards and using spreadsheets, here are some of the problems that that causes. Obviously all organizations do some level of analytics. I think everybody would say they are doing some types of analytics. The question is what were most common and least common. Financial information analysis was the most common, then comes cost information, then operations is third. And what you can consider more sophisticated or innovative or less popular analysis, things like risk, things like sustainability, I think that’s just a measure of sophistication of business analytics.
Using spreadsheets instead of a Business Intelligence (BI) system can lead to several significant issues that undermine data accuracy, efficiency, and decision-making. Firstly, spreadsheets are prone to human error, especially in data entry and formula calculations. A single typo or incorrect formula can propagate errors throughout the spreadsheet, leading to inaccurate insights and poor business decisions. Additionally, as spreadsheets grow in complexity with large datasets, the risk of errors increases, making it difficult to maintain data integrity and reliability.
Secondly, spreadsheets lack the advanced data analytics capabilities that BI systems offer. While spreadsheets are useful for basic data analysis and visualization, they fall short in handling complex data modeling, real-time analytics, and interactive dashboards. BI systems, on the other hand, are designed to process large volumes of data from multiple sources, providing advanced analytics, predictive modeling, and dynamic reporting. Without these capabilities, businesses relying solely on spreadsheets may miss out on deeper insights and trends that are crucial for strategic planning and competitive advantage.
Another significant drawback of using spreadsheets is the difficulty in maintaining data consistency and collaboration. Spreadsheets are often shared via email or cloud storage, leading to multiple versions of the same file. This version control issue can cause confusion, data discrepancies, and time-consuming reconciliation efforts. BI systems offer centralized data repositories and collaborative tools that ensure all users are working with the same data set, promoting consistency and seamless teamwork. The lack of a unified platform in spreadsheet-based workflows can hinder effective collaboration and data sharing.
Lastly, spreadsheets can pose significant security and compliance risks. Sensitive data stored in spreadsheets may not be adequately protected, especially when shared across multiple users and devices. This lack of robust security measures increases the risk of unauthorized access, data breaches, and loss of confidential information. BI systems, in contrast, come with advanced security features, including user authentication, role-based access controls, and encryption, ensuring that data is protected and compliance requirements are met. Relying on spreadsheets can expose businesses to legal and financial repercussions due to insufficient data security and governance.