In today’s competitive landscape, the use of forward-looking analytics is now mission-critical. Craig Kelly, VP of Analytics, Syntax, tells us: “If you aren’t getting more from your data, you are falling behind your competition, and you may find your company obsolete in the near future.”
What are the potential benefits of businesses gaining more actionable insights from their existing data?
Unlocking actionable insights from existing data can benefit all areas of the business by driving better efficiencies and unlocking potential opportunities, ultimately increasing revenue and reducing costs.
How can analytics provide enterprises with insights that will drive better decision-making? What sort of information can analytics provide to enterprises that would be especially useful?
Analytics today can help identify what will happen in the future with a reasonable degree of accuracy. This can include traditional concepts like sales forecasts, which now can incorporate more than internal historical data, but also is easily combined with third party external data that can help generate robust forecasts via Machine Learning (ML).
Manufacturing businesses may want to analyze capital asset usage in real-time, combined with other IIoT generated data to maximize the efficiency of preventive maintenance to ensure uptime and minimize costly repairs. Other companies may want to leverage predictive analytics to help drive customer experience and interaction with applications and websites. So really, the possibilities are endless, it comes down to focus.
How far can analytics help enterprises solve business challenges?
It really comes down to what challenges the business wants to focus on. With huge advancements in technology and making it more accessible and usable specifically with ML and Artificial Intelligence (AI), predictive analytics combined with automation can solve a lot of problems.
How can businesses draw up a plan of attack to leverage the data sitting in databases?
I think a good route is to start backward, by identifying areas of the business that are the most high-cost contributors or bottlenecks within business processes. Then you can begin thinking through what a perfectly automated system would look like if anything is possible. From there, you can begin thinking through where the source data lives and how it could be accessed even if it isn’t currently, as well as thinking through what the analytics architecture would need to look like to make it easily accessible to the appropriate applications to provide the necessary insights. Ideally, the architecture is elastic and allows for experimentation in a cost-effective manner.
What are the biggest barriers to enterprises making the most of their data?
There are a few major barriers to enterprises making the most of their data:
- Company culture can be a barrier because companies get trapped into just doing things the way they always have been, and they are reluctant to try new approaches
- An organization may lack a flexible architecture to experiment with
- Or they may lack vision or support from the business to identify areas of possible improvement
What are the dangers of IT leaders deprioritizing data analytics initiatives?
IT leaders often think of analytics as a ‘nice-to-have’ technology and frequently put it on the back burner while they focus on other mission-critical objectives and making sure the lights stay on. But in today’s competitive landscape, the use of forward-looking analytics is now mission-critical.
If you aren’t getting more from your data, you are falling behind your competition, and you may find your company obsolete in the near future.
How can companies break down data silos to deliver a greater degree of visibility?
Data lakes have proven to be the go-to strategy for eliminating data silos. They provide a central location for all disparate data where you can easily provide the appropriate security and governance protocols in place. Then, that data can be easily combined and transformed into structures that can be consumed via a myriad of analytics services – as well as traditional business intelligence consumption.
Are there any tools and systems available that successfully take the burden of data management off busy IT teams?
Cloud analytics technologies, in general, can help remove the burden from IT in multiple ways, including:
- Not having to worry about managing the infrastructure of physical servers
- Serverless analytics technology can remove on-going administration of operating systems and applications
- Having data in a data lake can ease security and governance controls of the data, putting the control in one place rather than maintaining from all the separate systems
- Data in a lake allows for more self-service and consumption of the data itself
- Many ML and AI services have been greatly simplified so that you don’t require specialists or data scientists to obtain the insights
How can businesses use their SAP ERP to gain more actionable insights from their existing data?
There are many analytic services and capabilities that exist outside of SAP, so efficiently extracting SAP data and delivering it to a data lake is a great first step. From there, adding supplemental data from other sources, automating the extractions and transformations, and making it all easily accessible to analytic services such as ML and AI in a cost-effective and elastic architecture is critical. There are many ways this can be done, but a few examples would be:
- Extracting sales history and supplementing the data with other data (internal or external), and leveraging ML to generate sales forecasts that are more robust than just native SAP forecasting
- Combine SAP manufacturing data, and combining with IIoT data to help predict issues with the quality of finished goods
How can SAP customers in particular leverage its tools to make data analytics less complex?
Data analytics can be intimidating and overwhelming if you try to boil the ocean and have the scope of your analytics project too large. It’s advisable to start with specific use cases that can realistically be solved with analytics so that you can get a win and start using the technology. From there, you can expand your scope and overall usage across the enterprise.
