How Can Data Analytics Help My Small Business – Part 1 – Data Analytics And Consulting
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In a recent data analytics consultation, I got asked how data analytics and data science could help my clients 7-8 figure business. We discussed what their business goals were and what tools they were currently using.
Based on that we set up the next steps to start aligning some data analytics strategy goals with their business goals. For example, step one was to start centralizing their various data sources so they can start melding these data sets to create tools and dashboards like a customer 360 dashboard that ties customer information from their online sales, build alerting for abnormal spending, and improve pricing optimization.
Now, this was just one client example.
We wanted to go over a few other ideas on where your company could have an opportunity to use data analytics, data science, and engineering to improve your business. We have broken it down into three sections, data science, data engineering, and developing data products.
Data Engineering – Where All Data Value Begins
Data science takes a lot of attention away from other very important aspects of data. For example, good data engineering and analytics. These are often the base layer any company needs before they can even begin to start doing data science work.
In fact Interview Query recently reported that the number of data engineering roles at companies continues to grow annually as surprisingly data scientists shrank back in comparison.
This is probably because, to even think about developing some algorithm, data needs to be centralized scraped from various data sources.
One of the main benefits data engineers bring is their understanding of creating central data storage systems often this will be a data warehouse, data lake, or the newly coined data lake house. All of these require some form of process to get that data from point a to point b(Also often called an ETL).
Having your data centralized allows you to start asking questions from multiple angles. You aren’t forced to pull an excel report from Salesforce, Zendesk, and workday and then copy-paste data which is a very error-prone process.
Also, this means you can automate how quickly your data is loaded into your final reports and dashboards since you no longer have to manually extract your data.
This is just the beginning.
You can quickly start to develop KPIs, customer 360 dashboards, and so many other tools that all focus on tying data together.
The ability to have all your data easy to access and standardized has countless benefits.
Our team is currently working right now to re-develop an admin panel to have both admin abilities + analytics that tie to each customer so the sales and support teams have a better understanding of who is using their products.
However, this is starting to bleed into the topic of building data products which we will cover at the end.
Data science
Data science continues to get a lot of the limelight when it comes to data consulting. It makes sense, there are thousands of articles discussing the impact data science has on businesses. I mean, we have written half a dozen ourselves.
This is because oftentimes these projects have the most exciting outcome. They can lead to massive cost savings or increased profits.
One great example of this is dynamic pricing.
Companies like Uber and Expedia have used dynamic pricing to optimize costs for both users and their services. Through a combination of historical and current data, these companies have been able to better price their services.
But optimized pricing is not limited to tech companies. Many other industries can similarly benefit from using similar techniques as large tech companies to better price their services. We have been able to help one such company in the transportation industry develop its easy-to-use tool that allows them to better manage to price.
It has provided the company an opportunity to not only increase revenues but also better manage employees and overtime as they are more aware of which days to accept heavy usage of their services and which days/months to reduce overtime hours. We have continued to work with this client further optimizing and analyzing other parts of their business.
Another great example is anomaly detection.
Using data mining techniques and statistical classification data experts can spot abnormal clusters and patterns in everyday events. Data represent multiple entities (patients, customers, businesses, pieces of technology, etc) involved in everyday interactions.
For instance, a patient going to the doctor interacts with a healthcare provider (like a hospital or an ER), doctors, nurses, insurance companies, etc. All of this results in data records that can be used to spot abnormalities. The sheer size of the data and the number of entities interacting can start to paint a picture of normal interactions. Maybe you notice it is typical for most people to receive an X-ray with a specific type of emergency procedure.
This might make it strange for a hospital to then not have the x-ray on the same claim. If this abnormality repeats itself at the same hospital, then it might be due to a doctor or biller upcoding claims. Upcoding is when a doctor claims to have done a more expensive procedure which leads to higher costs. For instance, if a doctor states that they treated you for a break when it was just a hairline fracture, they would be cheating the system and you.
Upcoding, overuse, unexplainable procedures are hard to see in the oceans of data insurance providers manage. The sheer mass of data can make skew patterns that the human mind can track. This is what the perpetrators who perform insurance fraud hope for. However, this doesn’t have to be the case anymore.
You can also use other techniques to help solve business problems pricing optimization and category recommendation (two more examples of how our team helped small and medium businesses last year with data)
Building Data Products
One area that isn’t always discussed when thinking about what to do with your data is developing a data product. I have personally worked for several companies where I have helped develop products from the ground up.
This is usually a combination of data engineering, software engineering, UI/UX, and data science.
But these data products can’t be built on tech know-how alone. Much of the value of these products comes from the subject matter expertise.
Subject matter experts in fields like Transportation, Insurance, Finance, and healthcare understand what is important to track and report on. As a technology consultant, my primary focus is developing a product from a technical perspective.
Yet, I have been able to develop products in multiple industries thanks to working directly with experts in said fields.
This is where the value comes in. My skills as a programmer helping to automate and scale-out the expertise of a subject matter expert and then that tool can be used by other clients.
So you could build a dashboard/reporting on industry KPIs and then sell it out to other people. You can see examples of these products in companies like Health Catalyst, HDMS, and Upboard. These companies make money by aggregating data from their customers and then distilling it into insights.
They do this by combining technology with expertise.
Is It Time For You To Upgrade Your Data Analytics Strategy
These were just a few examples of how data science, data engineering as well as understanding on how to automate expertise into a product could help your business. Even if you’re only in the 7-8 figure range.
Using data to make better decisions provides companies a competitive advantage. However, this depends on the quality of data and the robustness of data processes set up.
Our team can help you start or upgrade your current data strategy.
If you aren’t sure what you want to do with your data, then feel free to reach out and I would be happy to help outline some possibilities with you for free.
Drop some time on my calendar today!
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