The Benefits of AI in Data Analytics
Saving you time, money and effort
Data analytics is no fad. In fact, the global market for data analytics has been predicted to exhibit a CAGR of 30.08% between 2017–2023 to surpass a valuation of USD 77.64 billion. A large part of this is due to the increased generation of data during the period but far more because of the increasing ability to use statistical algorithms and machine learning techniques to deliver actionable results for businesses.
“People used to say that information is power but that is no longer the case. It’s the analysis of the data, use of the data, digging into it — that is the power”
From a business perspective, data analytics can be used to increase revenue, respond to emerging trends, improve operational efficiency and optimise marketing to create a competitive advantage. However, with so many buzzwords flying about such as data lakes, machine learning and artificial intelligence, it can be difficult to understand where the value is coming from and what an external provider can offer.
Structuring the Data
One of the most difficult challenges faced by organisations in the field of analytics is that data sources have historically been very difficult to analyse. As data sources are often disparate and fragmented, there has been a requirement for manual data cleansing prior to analysis. Studies show that this process of data preparation takes around 80% of the average analysts time.
In addition to this, much of the information generated by businesses has little or no formal structure; contracts, surveys and emails all hold a wealth of knowledge that analysts could use to uncover opportunities.
Trending AI Articles:
This work has often involved use of external consultants or significant investment in employee time. As a result, businesses require what we’d call an ‘opportunity cost’ and this is often restrictive or prohibitive in the adoption of data analytics or business intelligence platforms. That’s where text analytics comes in.
With the advent of machine learning, text analytics has advanced to a level where it is capable of exploring large numbers of interrelated features, bringing structure and clarity to documents and data. Taking invoices as an example, companies such as Rossum.ai are able to remove the need for manual processing and extract the key information into a structured table. But consider applying similar techniques to contracts, spend data and other usage data and it becomes clear that there could be a wealth of knowledge in analysing these datasets in combination; this is what VisionClerk do.
Performing the Analysis
When it comes to analytics Deep Learning is often raised a potential solution to automatically extract meaningful patterns from large datasets for decision making. However, the key here is truly defining and understanding the goals of your analysis. Pre-prescribed rules with specific logic and decisions are still invaluable in helping users uncover meaningful opportunities with a full understanding of where the information is coming from.
That’s where partnering with an organisation that focus specifically on the analysis you’re looking to perform can be advantageous. Businesses often face a dilemma between brining in additional employees or forming links with external partners; the latter becoming far more attractive with the relative scale enabled by cloud platforms.
Linking your data with companies who specialise in a singular pursuit and direct focus on the problem you are trying to solve can ensure that you get consistent insights into the most relevant opportunities for your business. This collaboration can help uncover unique perspectives that working by yourself never could, and expand your thinking beyond what you realised was possible.
Thanks for reading. And if you think 15 minutes of my time could help you, please do get in touch.