Data Analytics – The Usability Problem
Data is the oil and analytics is the combustion engine – Gartner
Data analytics has become so popular among businesses that Forbes estimates that about 53 percent of mid to large scale companies have already adopted it, with the number expected to rise to a staggering 80 percent by the end of 2020. As per research by Statista, the value of the software segment of advanced analytics and other big data services will increase to $46 billion by 2027. But while most organizations have implemented data analytics in their companies or are about to, their ability to leverage its power effectively is limited. This could be the reason why most data analytics projects tend to fail. Gartner estimates that 60 percent of big data projects fail.
The fact of the matter is, a data analytics project includes several processes such as data aggregation, data extraction, cleansing, categorization, pattern analysis, insights generation etc. While most of these processes are pretty straight forward, it is the insights that are tricky to generate. ‘Data’ is not the same as ‘insights’. And data on its own is pretty useless for any business.
Let’s consider an example. Company ABC’s data analytics system may have generated data that tells them that their trade promotions for product A have seen a steady decline in generating customer interest. But unless this data can be processed into insights that can tell us why the decline is taking place and what the company can do to change it, the data remains unactionable. Insights, unlike raw data, need to be actionable instead of just informative.
One may also think that once you have the data, you can easily glean insights as and when required from it. However, to go from raw data to insights, there are several highly complex steps to be covered. While the first step is data aggregation from multiple sources, this data needs to be cleaned of any redundancies and discrepancies. Only when the data is ‘clean’ is it ready for analysis. The analysis should generate valuable insights that are then communicated with all the business stakeholders in the organization for decision-making.
Needless to say, this is a not-so-easy and highly manual process. This is the reason companies have to hire highly qualified data scientists and analysts to glean insights from data through data analysis. BUT, hiring such experts is not only expensive but also difficult. There is a serious shortage of data analysts and scientists in the US and the global market in general. Even if a company manages to find and hire the right data experts, they are seldom business experts and need to be trained thoroughly on the analytics business users expect. So what should organizations do to ensure that the power of data analytics is maximized with minimal manual intervention – Augmented Analytics.
Understanding Augmented Analytics
Augmented Analytics helps relieve an organization’s dependence on data scientists or other manual processes by automating the insight generation process with the help of advanced machine learning and artificial intelligence algorithms. With an augmented analytics system you can automatically go through your company’s data, clean it, analyze it, and convert data into actionable insights for decision-makers with little to no supervision from a technical expert.
Gartner also recommends data and analytics leaders to plan and adopt augmented analytics as platform capabilities mature.
Augmented analytics can essentially cut down on constant human intervention in insights generation. It also reduces time-consuming exploration and weeds out false or less relevant insights automatically. We also need to understand that data analysts and data scientists, people entrusted with insights generation, are at the end of the day human and prone to making errors. But with the help of augmented analytics that applies a range of algorithms and ensemble learning to data in parallel, the risk of missing important insights in the data or making mistakes, is vastly reduced. The resultant scenario is an organization whose data analytics and insight generation process is streamlined and trustworthy.
The Different Facets of Augmented Analytics
In a paper published as part of the Gartner Data & Analytics Summit held in Sydney, Australia in February 2019, Gartner also talks of the different facets of augmented analytics, that include:
- Augmented data preparation – This uses machine-learning automation to augment data profiling and data quality, harmonization, modeling, manipulation, enrichment, metadata development, and cataloging.
- Augmented data discovery – Formerly known as ‘Smart Data Discovery’, this part of augmented analytics enables business stakeholders and citizen data scientists to use machine learning to automatically find, visualize and narrate relevant findings (such as correlations, exceptions, clusters, links, and predictions) without having to build models or write algorithms. Users explore data via visualizations, search and natural-language query technologies, supported by natural-language-generated narration for interpretation of results.
- Augmented data science and machine learning – This automates key aspects of advanced analytic modeling, such as feature selection and reduces the requirement for specialized skills to generate, operationalize and manage an advanced analytics model.
Additionally, augmented analytics is also touted to be a key feature of conversational analytics. Conversational analytics helps businesses generate queries, explore data, and receive insights using NLP (Natural Language Processing).
The Road Ahead
The volume of data that is currently being produced has reached proportions that data scientists don’t have the capacity to explore. hence manual data exploration may result in businesses missing key insights. But with the help of augmented analytics, companies can use an automated algorithm which are adept at exploring all possible hypotheses from the collected data. According to Gartner, more than 40 percent of data science tasks will be automated by the year 2020, resulting in increased productivity. Gartner also predicts that through 2020, the number of citizen data scientists will grow five times faster than professional data scientists. With citizen data scientists and augmented analytics working hand in hand, data insights will be democratized, becoming accessible to a wider pool of business users.
Acuvate helps medium and large enterprises generate actionable insights from their data with our advanced and augmented analytics solutions. If you’re planning to implement Augmented Analytics in your organization or want to learn more about the topic, feel free to get in touch with one of our data analytics experts for a complimentary consultation.