Business Intelligence (BI) as a field has gained a tremendous amount of space in the last few years. As companies generate more and more data, the need for structured systems that can efficiently store, process, and visualize data grows, and that is where BI solutions shine. For those who are unaware, BI can be conceptualized through the definition of the Tableau Software company, which describes it as:
[…] an infrastructure that collects and analyzes large amounts of data to give organizations a clear and comprehensive picture of their data. The goal of a BI system is to give stakeholders a clear and customized view of their data to empower them to make data-driven decisions.
Business Intelligence infrastructures have been bringing its adopter’s plenty of benefits, such as faster reporting, automatization of repetitive tasks, better business decisions, increased revenues, competitive advantages, and more. But all those advantages won’t come instantly, as businesses need to invest resources – time included – into their BI culture, and that is where we see businesses at different stages of Business Intelligence maturity.
Some companies, surprisingly, still don’t have BI solutions, others are still beginners and some have reached intermediate or advanced levels, but they all share one thing in common: they all still have problems that can be solved through Business Intelligence Consulting Services from the right consultant.
In this article, we will talk about common issues we see in companies of varied BI maturity stages that can be solved with BI Consulting Services, along with some examples of how we dealt with those complications. Stay tuned – maybe you will see your situation described here!
Peter Drucker, known as the father of modern management, once said that “if you can’t measure it, you can’t manage it”.
Many companies face a problem that aligns very well with what Drucker said: they don’t generate any data about their processes and operations. This is bad because it leaves management in the dark regarding the company’s current situation, not knowing critical information about their business, such as the number of units sold on that day, customer satisfaction, or their product’s rate of failure.
These companies are then left unable to effectively improve, as they cannot know whether their actions result in better results for those metrics or not, only having anecdotal proof of it (the famous “gut feeling”). This is why to manage and improve your business, you need to understand its current situation, and the best way for you to achieve that understanding is through data.
If you have good data about something – a manufacturing process, a sales department, or a whole company – and have the means to analyze it, you can easily understand its strengths and weaknesses and come up with plans on how to improve based on that. And that is where BI Consulting Services can help. It is through Business Intelligence that companies build the infrastructure and culture needed to generate and analyze the data needed to improve their processes in a healthy and scalable way.
Another problem that companies that haven’t hired a BI Consultancy usually have is the lack of predictability about their processes. To illustrate this scenario, I will be talking about sales, but the same logic can be applied to manufacturing, customer satisfaction, employee retention, and more.
In sales, it is common to have different results at different times of the year, and that is called seasonality. Most retail businesses have their best results around the end-of-year holidays, while school supplies will be at their highest at the beginning of and halfway through the school year.
This seasonality is acknowledged through historical data – companies track their daily sales records through many years and can then visualize that some months are consistently performing better than others, and that type of information is crucial for businesses, so they can plan goals, shifts, and stocks accordingly.
But seasonality analysis is only one of many ways that data can bring predictability to your business. Any pattern you can spot may be useful to bring more predictability to your business. An example would be a greater-than-usual increase in sales after airing a new ad that targets an untapped audience. That can be an indicator that this audience has the potential to bring even better results if you invest more into it.
And finally, businesses with more advanced BI systems will use them to fuel Machine Learning algorithms that will be able to predict, with a given accuracy, how much will they grow in the coming months. That provides decision-makers with a lot of information, so they can make the best possible decision at that time.
Another data-related problem that companies that do generate and analyze data may encounter is the non-reliability of data.
Let’s say that you’ve implemented a Customer Relationship Management software that tracks and stores your customers and sales data. This system works in a way that any analyst can export that data to Excel and perform whatever analysis they need to do from scratch, generating their reports and sending them to management, who will take insights from it. While this practice is better than having no data and no reports, it can cause major data integrity problems.
Those issues start when there’s no standard way to treat your data. You may find reports that will have conflicting results for KPIs, since different analysts may have different understandings of that metric, or may have simply made mistakes when handling data. You may also find departments arguing about what is the correct way to calculate a metric, and that may also decrease your employee’s trust in BI and data analytics, which is not good. And this complication runs even deeper when there are reports built on top of other reports, where you’ll need to untangle a web of mishandling of data to understand the root cause of your discrepancies, what will require work from your analysts, work that could be utilized for more fruitful tasks.
There are several ways to mitigate this trouble. The best one is through the implementation of a Data Warehouse (DW). Data Warehouses are a cornerstone of Business intelligence that compile and standardize data from various data sources through the Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT) processes. There are a variety of tools and online resources that will help you to build your DW, but that task may prove itself challenging. Also, Data Warehouses require consistent maintenance as your business grows, so it’s best to rely on experienced BI professionals to make that implantation as smooth as it can be.
What we described above refers to a situation that occurred with one of our customers. They were managing their data through decentralized spreadsheets, using both Excel and Google Sheets. That led to a general distrust in the reports presented, the lack of a centralized location to access the dashboard and reports, and also their analyst being frequently overworked.
During our time with them, we built a Data Warehouse for them using Qlik Sense, from the extraction of data from multiple spreadsheets and their CRM to the final dashboards that made it possible to perform complex analyses of historical data and basic sales predictions. That made it possible for sales management to properly identify which shops needed more resources and to act on it, and it also tremendously helped their analyst, improving her efficiency and her results.
It is not enough for data to be simply available, it needs to be available as quickly as it can. Decision makers cannot afford to wait too long to have the information they need, as that would imply delays in decision-making, and that could potentially cause missing a critical time window for a great business opportunity.
That can happen to all types of businesses, from the ones early in their BI practices that rely on individual analysts’ reports to the ones that have built a Data Warehouse, but that is inadequate to handle the volume of data that is generated.
Regarding the former, the situation is similar to what was described in this article’s problem #3. Suppose the directory board is trying to cut costs and will shut down its worst-performing store. They request their analyst to a report regarding all units’ performances to identify which one will be cut. Since there’s no standard way to store and process different stores’ data, this analyst will be left with the task of collecting data from multiple sources, compiling and standardizing it to a unified format to finally crunch the numbers to generate the report that the board required. If that business had invested in a proper Data Warehouse, the data would already be stored in a standard way across all stores, and it would be easily accessible to whoever needed to access it, greatly facilitating the analyst’s work.
As for the latter, the mere existence of a Data Warehouse is not enough to guarantee that data will be processed in an adequate amount of time. When companies grow, it is only natural that the volume of data that they produce grows too, so investments in new hardware are needed to keep up with that volume, but it is not uncommon for that not to happen. When this situation occurs, each step of the ETL process takes progressively longer, and it may reach an unsustainable situation where data takes too long to be available. Another reason for this delay may be that the Data Warehouse was built inefficiently. Not all BI Analysts and Consultancies know how to leverage their tools’ potential to build a high-performing solution for their end users, which may cause delays in data processing.
All of this is another reason why it is so important to invest in Business Intelligence consulting services. With proper DWs and ETL processes set up, you make the data you need get back into your hands faster, facilitating your company’s decision-making process.
We, at BIX, have seen this first-hand with one of our clients. They fitted well in the scenario where the company was still mostly dealing with decentralized spreadsheets as data sources, with no defined ETL process to fuel their dashboards. They made the first move in defining their ETL pipeline within Power BI Dataflows, and we helped them refine the process based on business requirements and what they wanted to accomplish. We also worked with their data to build datasets and reports in Power BI for almost every business area in the company, decreasing refresh time from weekly to daily. Most of the work from analysts regarding data was completely automated, and we’ve built complex analyses that weren’t available before, which led to better data-driven decisions being taken by management.
So, there you have it: 4 problems your company can solve with BI Consulting Services! If your business is struggling with some of the problems we described, you should take action to try and fix them, but be careful, as issues can be deeper than they seem to be. A lot of the situations we described earlier won’t present themselves isolated, so you’ll need someone with experience in BI to diagnose exactly what’s wrong and to define what’s needed to fix those problems.
You could try to solve them alone, but without the resources and know-how, you could be just setting yourself – and your business – to failure, so always trust experienced professionals when trying to improve your company’s Business Intelligence practices.