How to make better business decisions with data
Businesses are faced with hundreds of decisions on a daily basis. Throughout an organization, everyone from top-level executives to customer-facing employees must decide which of the choices they have are best, and all of these decisions matter.
Some decisions may be one-offs, some can occur more regularly. There will be times when decisions can be automated and times when they cannot. Regardless, most of the time the decision process can often be improved with the inclusion of well-structured business rules that prioritize data integrity and business goals.
Before getting into the details, make sure to sign up for the AMA: Enabling Business Intelligence with Data Integration webinar to learn even more about making business decisions.
Who Deals with The Data?
There are many different departments that can handle data in an organization. Some titles you may recognize include data analysts, quality assurance, accountants, economists, or market analysts.
Managers will have data of many different forms for operational decision-making. Marketing will look at data of different forms such as analytics from social media posts or visits to the sites. Sales will look at historical data and try to forecast future revenue.
Each one of these roles will have exposure to different types of data, tools, interpretations of the data, and goals within their departments, but really, each department has the same overarching goal which is to make better business decisions for their organization.
Having the Right Resources
Having the right resources is a big factor for companies because when you have the right people with the right resources the decision-making process can be made a whole lot easier.
One of my favourite movies (for a number of reasons) is Moneyball which is based on a true story and stars Brad Pitt who plays a general manager of a failing baseball team the Oakland Athletics. Starring alongside Pitt is Jonah Hill who uses a statistical algorithm to pick players for his team to sign. I would highly recommend watching if you like numbers. I don’t even like baseball, but I like data and boy does this movie show how the use of data and statistics can make a difference in a business which baseball teams basically are.
The term Moneyball was coined to describe the method that Hill’s character uses to determine which players the Cardinals should trade during the season. This system has been adopted by a lot of baseball teams and has even crossed the pond to Europe where football clubs like Brentford (UK) and FC Midtjylland (Denmark) have seen great success using the system.
In this case, Pitt has the right resources in Hill and the algorithm. They then just needed to make the right decisions to win the games (I won’t spoil the movie as it is a great watch). Resources can take the form of particular employees that fill certain roles or they can be software-related. But now it’s time to ask yourself: do you have the right resources in your company to make the game-winning decisions?
Applying Business Rules
Business rules are statements that tell you whether you may or may not do something. They are directives that define business activities and should form the criteria of your decisions to help your organization reach their goals. These rules should then, therefore, guide day-to-day decisions making processes.
The implementation of business rules ensures that decisions are made with consistency and clarity. Without business rules, decisions are made on a case-by-case basis which can lead to a slow and inconsistent process.
A real-life example may look like this: If your company has partners that sell your product or provide a service they can often be tiered as Bronze, Silver, Gold and Platinum. These tiers often have goals set out by the company that the partners must meet to be classed in these tiers. If a partner reaches the criteria set by the company they are rewarded with the equivalent status. This helps the company and it also helps the partners as they have a goal to aim for and clear expectations of how they will be recognized.
Other types of business rules fall into categories like regulatory or legal rules that may apply to certain states and regions depending on where your company is located. For example, rules in Canada will differ from the rules in Europe.
Making Decisions with Data
The steps below outline generally what to do to make better decisions for your business. This process can be simple or extremely complex. Whether you’re dealing with few data sources or many, it’s important to have a deep understanding of your data to grow your business.
1. Validate Data
It is key to ensure that the data you are using to make the business decision is accurate to start. Skipping this step could lead to a costly mistake further down the line. When you receive data from one source it may not always be in the best shape (poorly treated or bad data is a leading cause of most poor business decisions).
Validating data before integrating it into a workflow can save time, set standards, and mitigate errors. Data validation can be quite a complex process usually performed using a scripting language such as Python to write scripts for the validation process. However, there are also no-code platforms that can be equally as powerful for those unfamiliar with Python or other languages.
2. Integrate Data Sources
Once data has been validated, you can integrate the data into your workflow. This step is where you would integrate the validated data from the different sources in your organization and break down those walls to the gold mine of insights.
Connecting to different sources can produce its own set of challenges. Some employees may not have equal access to databases, servers, web services, and API’s that an organization uses. And like the Python scripts, they can require a certain amount of knowledge and training to understand. But, again, no-code integration platforms exist and can replace the need for scripting knowledge so anyone can integrate with databases, web services, and APIs.
There are many complexities to each marketing, financial, or strategic decision. Understanding a customer’s needs and desires and aligning products to these needs and desires can be a winning formula for any business. By incorporating data from various sources and integrating them together, forecasting and trend analysis can be done to help your business grow.
3. Analytics
Depending on the types of data you are working with, be it spatial or statistical, analysis of the data will be needed during the process. At this step, data can be analyzed to determine what is really happening behind the scenes of an organization. Often these analytics are performed using tools like Excel and Tableau. Automation can fit into this task once data has been validated and integrated into the workflow, allowing you to perform statistical analysis or spatial analysis depending on the formats that you are working with.
4. Visualize
As the old saying goes, “A picture says 1000 words” and the same is true about visualizing your results. Take all that data and paint a nice picture that is clear, concise, and to the point. Visualization really opens up the world of data to a lot more people. Some people might run a mile from a spreadsheet, but a colourful well laid out graph is a lot less intimidating and can convey the results clearly for anyone to see.
Getting Started
Often connecting the dots (or in this case, data) to understand the full picture can be a difficult task. You may be dealing with different formats you’ve never even heard of.
This is where FME can step in to save the day.
FME makes it incredibly easy to connect data sources allowing you to utilize all your data. There are a number of different methods for connecting to your data using FME including reading files from a directory, connecting to databases, or connecting to web services or APIs.
FME has broken down the walls to your data silos and relieved the gold mine of data for us to use in our decision-making process. As mentioned in our steps above, validation will be imperative to a successful workflow. FME can clean up and format data to ensure that when you perform analysis on the data that your results are accurate.
FME is a fantastic tool in this respect as it can handle both statistical data and spatial data which allows users to really expand the scope of the decision-making process. FME can write to a number of business intelligence and reporting tools including PDF, email, and Tableau. As I mentioned above there are numerous parts of the process where automation can be leveraged and that is the beauty of FME — it can perform all of these steps automatically.
To try FME yourself download a free trial.
To learn more about data integration and businesses practices, check out these resources:
