Q&A with Annrai O’Toole, CTO, Europe, Workday
Businesses have known for a while that data holds the answers to questions around improving performance, but how is cloud computing taking this to the next level?
For decades, businesses have been using analytics solutions to unlock business insights which inform their decisions. But as technology evolves, so too do the analytics solutions. Whereas ‘business intelligence’, as it is commonly known, has traditionally focused on analysing past data to make educated decisions for the future, the combination of cloud computing and in-memory computing (IMC) has, today, led us to the next generation of analytics which can deliver actual recommendations to businesses on their next moves.
So rather than just looking at past data, modern analytics solutions are instead made up of three broad subsections: descriptive (providing insights into what happened), predictive (predicting what might happen in the future), and prescriptive (delivering recommendations to business as to what steps they should take next to deliver optimal results). By using advanced data science and machine learning algorithms to provide leaders with insights, predictions and recommendations, businesses are armed with information to make smarter, and more strategic, financial and workforce decisions.
[easy-tweet tweet=”Modern #data #analytics are either descriptive, predictive, or prescriptive.” user=”annrai and @comparethecloud” usehashtags=”no”]
So, no more second guessing?
In the past, many important decisions have been made based on a limited amount of information or instinct but these days are gone. Recommendations based on algorithms which analyse accurate and current data allow us to make confident and assured decisions.
We are already experiencing these analytics solutions in our everyday lives. Take, for example, every time you log into Amazon or Netflix. The websites recommend books or films for you based on previous items you have purchased or viewed. The impact of having access to this information is extremely powerful in the decision making process.
What is stopping enterprises implementing this in a professional environment?
Businesses face challenges which are much more complicated than simply deciding which book to purchase next. If they are to obtain recommendations to answer the big questions such as ‘how do we increase revenue?’ or ‘how can we retain our talent?’, then they will require huge amounts of data and the analysis of it will be much more complex to get the right results.
Legacy systems are clear roadblocks to enterprises adopting modern data analytics solutions
But of course, access to this insight relies on having your data in order first. Legacy systems are clear roadblocks to enterprises adopting these analytics solutions, given that traditional ERP systems are situated across multiple servers and databases, with siloed processes and systems. As such, there is no unified view of a company’s data. The first step, then, for businesses is to move scenario data into a data warehouse or business intelligence application before the enterprise can reap the full benefits of analytics.
Talent retention is one business challenge many companies find particularly problematic. How do prescriptive analytics help remedy this?
At present, knowing when an employee is ready to leave is often based on a gut feeling from their manager. However, losing talented employees not only has an impact on quality of work and customer satisfaction, but it can also cost the company thousands of pounds to replace them, having a significant cost to a business’ bottom line. With so much at stake, surely manager instincts are not the most strategic solution.
Using data is a much more accurate way of understanding the top risk factors driving staff turnover. By accurately assessing factors such as time spent in current role, number of job functions held, or time between promotions, as well as highlighting which teams are at the highest risk of turnover, businesses are armed with the right information to support decisions to drive successful business outcomes.
If businesses were to implement these solutions, will they need to hire data scientists to make sense of all the information?
[easy-tweet tweet=”If the thought of algorithms leaves you scratching your head, you needn’t worry…” user=”annrai” hashtags=”bigdata, analytics”]
If the thought of algorithms leaves you scratching your head, you needn’t worry – it isn’t as complicated as you might think, and you certainly do not need to be a data scientist to understand the results. All the work happens behind the scenes and once the data has been analysed, it is presented to a HR manager or finance team in a single, intuitive dashboard. Making the results easy to understand is crucial to the success of analytics solutions, and only then will they deliver valuable insights to managers in the business.
So the technology becomes the trusted advisor to the business, does this not take away the role of the manager in the decision making process?
In a business environment, it is important to remember that these machine-produced recommendations should not run in a vacuum. Managers still play a crucial role as they ultimately have the final call on a company’s next steps. The prescriptive analytics merely act as intelligent guidance to support the decision making process rather than replacing it all together.
What needs to be in place for this new breed of prescriptive analytics solutions to thrive?
Accessibility is key. Companies depend on intelligent insights to grow their businesses, but if managers struggle to get access to the information then it becomes pointless. Data doesn’t have to be difficult. The next generation of analytics provide the right people, with the right information at the right time and consequently add real value when it comes to supporting growth in a business.
Data doesn’t have to be difficult.