What’s the worst thing business leaders can do with their business intelligence reporting and analytics? Overcomplicate it.
With all of our artificial intelligence (AI) advancements, sleek software for app UI design, bot development, and cloud infrastructure management tools, it can be easy to forget: behind all the technology, real humans are sitting in the seats making real decisions – and we don’t process information nearly as quickly as our devices.
Even the most sophisticated big data analytics implementation won’t be worth much to your business if you can’t get a handle on business intelligence (BI) reporting and analytics an actual person can decipher.
Reporting analytics are two separate, but dependent functions in your business intelligence. Think of it this way:
Reporting looks back at what already happened, and presents the facts.
How much was sold?
How many employees?
How many production errors?
Analytics look forward, using the data from reports, to present potential outcomes.
When should the business add more employees to reduce errors?
How can the number of sales professionals influence sales volume?
Looking back at what happened in the business, or forward at what might happen, is difficult – if not impossible – to do if business leaders can make sense of the data.
Business Intelligence – Reporting
Taking a massive amount of data and regurgitating it into an equally massive report simply isn’t useful. Instead, your BI reporting should achieve two important attributes:
- Deliver information ready to run. The reported data must be readable for an actual human, and ready to be analyzed in its current form if this is early stages reporting.
- Inspire action. Okay, your data can’t make anyone take action, but it should present clear (and hopefully compelling) information – including analysis if it’s at that stage – that enables an informed decision.
Business Intelligence – Analysis
Executing the analysis should be just as simple as reading the report. Remember, the user’s brainpower should go to making the decision, not deciphers how to force a BI and analytics platform to ask a business question and achieve an outcome.
Designing a flexible BI platform that can grow with a company and adapt over time may be simple in its UI, but powered with artificial intelligence and natural language processing function behind the scenes. In the spirit of keeping simple: in other words, this means users can ask or type a question, instead of requiring your VP of sales to use code to ask a question
Once you start throwing around words like ‘advanced analytics,’ it may seem like the phrase ‘now hire a team of seasoned data scientists’ will follow. But it doesn’t have to. Say it with us again: keep it simple.
According to the Harvard Business Review, success is more about blending commercial expertise with effective analytics, and this can be accomplished by:
- Don’t implement a widespread predictive analytics rule out of the gate. Instead, chose a single issue or area where analytics could quickly and clearly impact the business. This helps with a visible, quick win, and can narrow down the options.
- Remember the bottom line. At the end of the day, businesses want to make more money and waste less of it. Target your analytics at understanding and improving the most impactful areas.
- Know your people. Which employees or departments would make the most impact armed with a little more information? Those are the people to empower with the data first – just don’t forget to train them to use the data.
What to expect from a quality, expert software provider for your BI solution:
The right technology partner will take the time to understand your unique business needs and key players to make sure the software solution they implement makes sense. At TechGenies, our Genies have helped implement better BI solutions at multiple companies, and they stick to a few best practices, including:
- Develop reports for analysis. This means not only is data conducive to quick analysis but also that the labels make sense. That may seem obvious, but this takes planning. For example, a sales report might show YTD%, which could be the percentage to target, or the percentage the prior year, depending on who you ask. More thoughtful planning – and understanding of the analysis to be done on the metrics – could lead the developer to label the column YOY % Increase to better clarify.
- Streamline fields. Your developer should ruthlessly cut fields that won’t add value – and should not keep “nice to have” fields in place. Too many columns can make things more confusing for users – which impedes on the first goal.
- Protect sensitive data. Not everyone needs access to every report. For example. If you’re using artificial intelligence in the banking sector to predict account growth, only the people who need to see that data should be able to download those fields. Protecting medical information is even more important. Building in role-based security to protect entire reports or restricted fields can help build in compliance.
- Acts neutrally. Enterprise data is scattered across systems, some more organized than others. A good reporting layer is source neutral and can allow all the sources needed to form the report.
Empower your people with a functional, effective BI reporting and analytics solution. TechGenies can help: contact us today at email@example.com to talk to us about your business questions, and how we can help you build a BI solution that answers them.