Are you still thinking about adopting data analytics into your business OR do you actively jump on the wave?
It happens a lot that mid-sized or smaller companies hold off from advanced data analytics and big data projects. This is because of the simple reason that they simply cannot see the future advantages and the uncertainty on the return on investment.
In the previous blog post we’ve already explained the basics of what leaders should know about advanced data analytics.
In this article, we will dive deeper into how to minimize the risk on overspending or crashing the project due to an organizational misalignment.
Be clear about your top goals and success criteria
1. Business case first
A data analytics project roadmap starts with defining a business problem. What issues, operational inefficiencies must be addressed? What success mean to you? You must be clear about your top goals and success criteria.
Starting your data analytics project with a well-defined business case creates a much safer situation when trying to solve a business problem, and once it is identified it will be addressed with a possible data analytics solution.
In some cases, the outcome can be that the analytics techniques to solve the problem are too costly or complex in comparison to their benefits. A simpler solution might fit better to fill the gap.
A well-defined business case is key to a successful data analytics project initiation.
...what we want to identify is if the data is qualified to implement the desired data technique.
2. Find and define the data solutions
To find and define a data analytics solution for the business case you have to examine all available data sources, internal and open sources, to determine what data is stored, where and how; and verify how well it’s integrated into your technology landscape. At the end of the day, what we want to identify is if the data is qualified to implement the desired data technique.
This sounds complicated but it’s nothing more than baking an apple pie and check upfront if you have all ingredients, the tools, and the right oven to bake a pie.
It’s common sense to let an expert work on this subject and come up with the most suitable data solutions and techniques, because it will give you a better outcome and gains in the long run. Read more about the expert in point 4 Lease an expert mind.
The numbers out of the business case definition and the recommended data solution will be the factors to estimate the return on investment. But there is one more important factor, if not the most important one, the organizational capacity.
3. Organizational capacity
Most IT and Data Projects fail because the project did not consider the human element into their success planning.
Your organizational capacity is an essential factor to consider when deciding which data solution to implement, estimating the ROI and its success.
Call it Change Management or the trending replacing term Organizational Agility, either way, your people are the end-users, and they need to happily accept the change to the new solution.
Also, you should consider the extra costs associated with your team’s learning curve on the new solution and the one corresponding to the newly chosen solution maintenance.
Is your organization ready for a completely new system, or should your data solution must be simply a highly effective tweak?
Objective assessment and recommendations of an experienced data scientist
4. Lease an expert mind
Although your IT team can help you with the data analytics solutions, it’s recommended to consult a senior level expert in this field.
This expert should supervise the examination of the technology landscape, the data sources and enhance data quality, select the analytical techniques, the model coding, and should have a good understanding of the business side too.
An objective assessment and recommendations of an experienced data analytics profile will help your IT department and yourself, with a clear understanding of the right techniques and feasibility of the data analytics solutions.
5. Estimate the return on investment
The parameters to work with in a return on investment formula does vary per business case and data solution. For instance, a business case can be specified to save you time, reduce cost, increase revenues, or make better decisions, faster.
If we take for example a timesaver, you can calculate how much a person cost you per month to perform a task. For better and faster decisions, you can put a value on what it will cost if you take the wrong decision.
Taking basics of calculating any return on investment it will come down to this simple formula:
ROI % = (value gained – value spent) / value spent)) x 100
As you can see, each return on investment for your data analytics solutions must be calculated individually to determine the value gained and value spent.
The input for those values needs to come from your business case where you estimate the value gained for example from a cost reduction.
The value spent parameter must come from the data solution where the implementation cycle time, cost of the solution and organizational change will be the input.
Once implemented, the optimization of the model (system) accuracy will help you further in terms of ROI calculation, and of course, to match your expected gains.
Keep it simple and effective
Final thoughts
It might be overwhelming for you as a decision maker to get your head around data analytics projects. However, this should not stop you from jumping on the data-driven business wave. Without any doubt your competitors are on it or getting ready to ride the wave.
But if you and your organization are new to data analytics, then it’s recommended to get some help from experienced people who can guide you through the process. This will reduce failure and a lot of headaches.
Furthermore, there is no need yet to make huge changes or even think about sophisticated new systems. A new system or software is doing nothing more than using your already existing data and only raise the cost of implementation, learning and maintenance time.
Just keep it simple but effective.
Please don’t hesitate to contact us if you can use help with a structured data analytics solutions project.