As AI and Big Data continue to become commonplace in a range of industries, there are still some lingering concerns about how effective its implementation may be in various organizations. While these doubts take a variety of forms, they essentially boil down to three primary concerns. Let’s take a look at the three concerns and the ways that the predictive analytic tool, Absolute Insight, address and overcome them.
Misconception #1 – The data is not reliable enough
There is no substitute for clean data. It’s required for effective decision making, strategy forming, and business execution. Unfortunately in legacy systems much of the data is incomplete and inaccurate – or in our terms – dirty. Currently, the reliance of agencies and organizations on legacy systems and the data they contain is still very high which hampers the adoption of big data application.
In analytic projects, accurately estimating the effort required to clean the legacy data is like trying to judge the mass of an iceberg hidden beneath the water: difficult if not impossible. Fortunately, one of the strong points of Absolute Insight is its ETL (Extract, Transform, Load) capabilities. Put simply, the tools needed to clean data simply, efficiently, and effectively are built into the application. Most importantly – in addition to streamlining the data cleaning process – once completed, the process can be re-used as data is refreshed and renewed for additional analyses.
Of course cleaning data isn’t enough. In addition to cleaning your data, Absolute Insight makes sense of your data –both for instant business decisions and for future planning. Volume of data isn’t an issue here either: the larger the data sources the better the models become.
Misconception #2: It's too complex to be useful by operations staff such as Fraud Unit Investigators and Auditors
Big Data has traditionally been viewed as territory fit only for data scientists. The mathematical and technological skill set required to effectively use it has put it out of reach of many individuals who would benefit from it. Making it accessible to the average person is at the core of what makes Alivia Technology’s approach to predictive analytics so unique.
An easy way to think of Absolute Insight is that it’s your own personal data scientist. The goal at Alivia Technology is to free your staff from the need to master the programming and mathematical needs associated with predictive analytics. We supply the data analysis tools and training; your staff provides the subject matter expertise to best leverage the data.
Who are subject matter experts? These are people that currently exist within your organization who understand your business, your regulations, your challenges, and your goals. By arming them with the power of data, they will be in better position to take action immediately and efficiently compared to a data scientist who may lack the operational experience and savvy.
Misconception #3: AI isn’t financially feasible
AI can be expensive and user adoption low when a full blown solution gets “implemented”. It doesn’t have to be that way.
It is the partnership between the AI provider and your subject matter experts that allows for big data analytics to be delivered in an extremely cost effective manner.
Absolute Insight’s architecture utilizes lower cost (but highly powerful and fast) Apache Spark technology and state of the art visualizations to bring predictive analytics to the desktop of subject matter experts. By constructing libraries of templates and wizards, it allows users to employ the complex algorithms by merely selecting a course of action. By sidestepping the need to have every piece of data analyzed and disseminated by data scientists, this new approach saves money, while increasing speed and improving overall efficiency.
So there you have it. There are certainly barriers to predictive analytics, but fortunately with the right product, they are easily overcome.
Come learn more about how Absolute Insight is changing the way organizations approach analytics by signing up for a free demo!