For many companies out there, the rapid evolution of modern-day analytics is a far cry from decades past. Companies used to be considered ahead of the curve if you had the ability to accurately report what was happening in your organization weekly or monthly from an operational perspective – and you were light-years ahead if those reports were automated. However, in the analytical landscape of 2018, if your organization and the decisions made are not data-driven, you are behind said curve, in the analytics journey.
The Keys to a Successful Analytics Journey
A point we commonly drive home with our customers and prospects is that analytics is a journey, and with this journey comes an iterative cycle of diving deeper into data and applying new techniques to extract business value. Success in analytics today means having a single system of intelligence, backed by a robust data store that collects and aggregates all the disparate data points from all critical systems of record. Success also requires a solution that can rapidly realize value, where speed to innovation is paramount. The methods and data as part of analytics should be constantly changing based on findings within the data.
Common Struggles of the Analytics Journey
Embracing analytics in this way requires discipline, and a serious commitment of time and effort from many stakeholders at different levels of the business. This is often the biggest struggle a company faces when moving to the iterative nature of this solution.
Another battle can be defining success criteria and ownership of where the organization is today compared to where you would like it to be weeks, months, or years from now. It may sound obvious, but ensure you build a solid baseline of KPIs and related metrics. This will enable you to accurately measure the ROI of the actions taken as part of your analytical investment, and tie those KPIs to committed stakeholders.
However, the most common struggle is the part that occurs after the analytics are in place. After the data analysts provide precise and meaningful insights to the business, dazzling you with bits of knowledge that were completely unknown, the immediate follow-up is always the same: “now what?”
How does one move from identification to remediation? This exact question was posed by one of our recent customers, and as we’ve worked alongside them we’ve learned this question is answered through ownership, accountability, and measurement.
Ultimately it is necessary to move to a culture of prescriptive analytics, where the outcomes are not merely observations or identifications of key insights but instead actionable measures directly in line with key business initiatives.
Moving from Observations to Actions
Why do we end up in a state of analysis paralysis when the insights offered are so eye-opening? Sometimes it might be that the business and process inefficiencies the data highlights are not tied to a single business unit or service area, and cannot be remediated or solved by a single director. This is a good thing rather than a problem. If the issues you’ve identified can only be solved by the involvement of folks at the VP level, that means the impact of the action will be that much larger.
Once an organization has embraced the journey that is analytics, the next step is identifying outputs that the business can leverage. With robust analytics solutions such as Numerify comes the ability to address and answer complex business problems that align with the most important objectives of executive leadership. The problem is sometimes that even if the answer is right in front of you, acting on that insight is a whole other issue entirely — this is a big hurdle we help our customers overcome. Because Numerify has deep connections into many key data sources, insights are fully understood, multi-faceted, and can be tied directly to root causes.
Another way Numerify helps companies drive actionable insights is by identifying where they are today, where they would like to be next year, and establishing measurable milestones along the way. This is critical for a path to success. Another key part of this iterative cycle involves changing the type of analytics you’re leveraging. This includes the natural move to predictive analytics, and applying machine learning to past data sets to make forward-facing business decisions.
With Numerify, the time to value realized by our customers is vastly smaller than if they did it themselves. Like a salesperson who shortens their sales cycle to achieve positive results faster, so does Numerify shorten the iterative cycle of analytics. By managing and automating the data connections and the ETL, new data sources can be quickly leveraged to bolster existing analysis.
Data analysts and BI teams that need to worry about all aspects of data analytics spend on average 80 percent of their time sourcing data, building ETL, and maintaining the data store. With our solution, these experts can focus on that remaining 20 percent where the business value resides, and help maximize the value to the business.
Bringing It All Together
Numerify is uniquely positioned to help customers usher in a data-driven mentality when analyzing and making decisions for their business. As an analytic partner, Numerify remains in lockstep with their customers no matter what step of the analytic journey they may be on, and provides tried and true practices to drive true action from the invaluable data within.