As data volumes increase and become more complex, it becomes ever more difficult to identify the most accurate, relevant and actionable findings. Enter augmented analytics: a true game changer.
An approach that automates insights using machine learning and natural language processing, augmented analytics is changing this manual process and marking the next wave of disruption in how companies create, interpret and share data. With this transformative approach, data analytics professionals will spend less time trying to understand data and more time finding the most relevant insights to share with executives and key stakeholders than with manual approaches, enabling companies to act in a more responsive, agile manner.
In this article, I will explore how augmented analytics is transforming the very important role of interpreting data in the enterprise, and how organizations can drive even more value from their data as they undergo .
First, let’s look at the key benefits of augmented analytics, which are disrupting traditional business models:
* the data preparation and discovery process
* data analytics for less business-savvy users
* adoption of actionable insights for the executive team and across the entire organization
What does all this mean to businesses, and why should they care? For organizations to compete in the Digital Age, data is the key to gaining relevant, actionable insights. However, the process of generating meaningful insights is complex for a few reasons. First, data scientists are a rare commodity — particularly, those who have honed skills in data science and a keen understanding of business models and operations. Additionally, a data scientist’s time is valuable, yet most of this time is spent manually preparing data through cleaning and labelling. This shortage of time and capacity means that most data analytics is performed on a small subset while a large portion of data assets aren’t mined. Let’s explore the benefits listed above to see how truly disruptive augmented analytics are.
1. Accelerates data preparation and discovery. When performed manually, data preparation is a cumbersome, complex effort to say the least. When data scientists have millions of records to comb through, simply finding all customers from a particular region, for example, can literally take several months. Not only are there huge volumes of data to examine, but not all answers are consistent, and reconciling them into one unified format can be daunting. For example, when looking at customer records to find people who are from San Francisco, different formats to answer this may be used – e.g. San Francisco, SF, San Fran, etc. There will likely be misspellings to factor in as well. Augmented analytics uses machine learning to look at all combinations of data to determine where similar items that are not exactly the same should be grouped together, as one example.
By automating these iterative steps, the entire data preparation and discovery time can be shortened by 50-80%. Imagine how much more productive and efficient your data analytics team could be if this were the case!
2. Democratizing data analytics. After the data is prepared, augmented analytics detects signals that impact business models but that less business-savvy data scientists may not be skilled at finding. Augmented analytics relieves data scientists of having to determine the appropriate algorithm to use and to write code to get results. An augmented analytics toolset will run eight to 10 algorithms on the data and fit it into a common format to detect patterns and outliers. For example, a large enterprise may want insights into billing and invoicing from their vendors. Based on the underlying data, augmented analytics will start detecting patterns and will automatically create an outlier analysis to detect that a particular vendor, which consistently invoices the company $100,000 per month, recently sent an invoice for $300,000. This automatic flagging of such an anomaly will prompt the data analytics professional to investigate the situation more deeply, removing the burden of first writing the algorithm to make this discovery.
In other words, augmented analytics democratizes the insights, making it easy for business users to extract complex insights and saving them significant time in doing so.
3. Enabling adoption of actionable insights for executive team and across the entire organization. Once signals and patterns have been determined, the results need to be communicated with executives. Traditional ways of sharing data, such as creating a report or dashboard, puts additional burdens on executives who don’t have time to log into a platform and do their own data interpretation. With augmented analytics, the tool will read the chart or report and translate the information into natural language statements such as “You are losing market share to competitor X, a trend that has been ongoing over the past number of months.” Team leaders can get answers to questions such as “What are top 5 things I need to focus on this month?” Providing this level of actionable insights enables data-driven decision making and increases adoption across the entire organization.
Sophisticated analytics systems offer voice and natural language processing capabilities, and are embedded into enterprise search interfaces and BI platforms. Imagine this: data scientists can interact with augmented analytics platforms using natural language simply by asking a question, such as: “how does our market share compare with our competitors?”
Augmented analytics addresses behind-the-scenes complexities making the data analytics process simple for business users and citizen data scientists alike.
Whether companies should adopt augmented analytics is not the question – the question is when to start, and how. The market is flooded with different tools for the different steps discussed above, and there is no one tool that addresses all stages or fits all companies. The best way for businesses to proceed is to work with a consulting partner that has broad experience in analytics and in emerging augmented analytics toolsets and methodologies to create a custom journey. There is no cookie-cutter approach; it is the combination of people, processes and technology that need to come together to create the appropriate path forward.
Augmented analytics is still an evolving field. At this point, most companies are not adopting augmented analytics for the entire end-to-end process but are starting with one small piece. In the next few years, I expect that that will change, and organizations will be using augmented analytics for the entire data analytics lifecycle. For now, it’s important to know the significant benefits augmented analytics provide: speed, democratization and broad adoption. With these capabilities, enterprises are better equipped to anticipate customer needs, improve business processes and prepare themselves for competitive success in the future.