Despite the growing presence of data analytics, organizations haven’t managed to leverage its power to the fullest and this perhaps can be attributed to the failure of most data analytics initiatives.
Gartner estimates that more than 85 percent of big data projects fail. Datasets over time have increasingly grown in size, become more complex and dynamic, and traditional BI solutions haven’t kept pace to manage them. The failures in data analytics are quite evident, they either fail in getting the data, dealing with it, preparing the data or perhaps even comprehending the data.
Traditionally, processes involved in preparing, exploring, and operationalizing data, have been primarily manual. As the data volumes are skyrocketing, the traditional, manual process of data analytics becomes inefficient due to the complexity that emerges with the ever-increasing size of data. Data science processes, in addition to being manual, require specialized data scientists who aren’t available in abundance. In fact, 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.
Augmented analytics has proven to be the elixir to this problem. Gartner postulates augmented analytics to be the future of big data and analytics. Augmented analytics is an amalgamation of natural language generation, text mining, natural language processing, and automated data processing capabilities in Business Intelligence (BI) systems. It helps users to precisely gather appropriate data, arrive at coherent patterns of information, model the data for analysis and discern BI insights.
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. It can also cut down the potential errors and inconsistencies produced by human intervention to generate insights. Augmented analytics is invaluable to deliver unbiased decisions and an objective picture of the scenario, thus transforming how users interact with data, consume data and materialize insights.
Augmented Analytics transforms and automates three stages of Business Intelligence which are currently performed manually by data scientists and are prone to human error.
Modern Business Intelligence systems with advanced analytics capabilities are capable of analyzing large amounts of data. However the process before analysis – data cleansing is still extremely manual and needs to be performed by data scientists. They have to manually develop metadata and ensure data profiling, quality, modelling and manipulation. As a result the possibility of human error increases even before data analysis.
The augmented data preparation facet of augmented analytics enables automation and self-service of data preparation. With machine learning, it can identify metadata, recommends best practices for profiling, cleaning, manipulation etc. This accelerates the data preparation phase and increases the productivity of data scientists.
Discovering patterns in data
With Modern BI and analytics platforms, users can easily find the data they’re looking for and visually explore data patterns and relationships. But the limitation is that users may not be able learn hidden data trends and deflections they’re not aware of and that can impact the business. This problem becomes more prevalent when the complexity and size of data increases. Users continue to focus on data exploration with their own preconceived notions and past experiences. Or they’ll have to manually explore all the potential permutations and combinations. This process is clearly time-consuming and there is a high chance of users missing important insights.
Augmented data discovery leverages algorithms to identify outliers and correlations in data. It automatically applies relationships to data and thereby lowers the chance of missing out on important insights.
Operationalizing insights from data
Most Modern BI systems offer advanced visualizations and interactive dashboards. However, not all business users can comprehend what is truly important in this data. Augmented analytics platforms use Natural Language Generation (NLG) to inform users about the most important findings in the data that they should be aware of.
According to research by Gartner, it is predicted that by 2020, owing to the automation of data science tasks, citizen data scientists with their ability to augment data discovery and other data science tasks, will surpass data scientists in terms of the amount of advanced analysis they produce and the value derived from it.
Data is being produced at such large scales today that exploiting the resourcefulness of data is exceeding the capacity of traditional data scientists. This results in businesses missing out on valuable information during manual data exploration.
Augmented analytics hastens the cumbersome process of data exploration and detects false or less relevant insights. By concurrently employing a range of algorithms and showing actionable findings to users, augmented analytics reduces the risk of omitting important insights that can be extracted from data. It also optimizes resulting decisions and actions.
Gartner also predicts that through 2020, the number of citizen data scientists will grow five times faster than professional data scientists. With concerted working by citizen data scientists and augmented analytics, data insights will be made available to a wider pool of business users.
Augmented analytics employ machine learning to automate several processes in the data value chain like data preparation, discovering insights and sharing insights with business users, operational workers, and citizen data scientists.
Several elements of an augmented analytics tool come together in order to help perform augmented data discovery, augmented data science and machine learning, and augmented data preparation.
Here are some of the key elements that an augmented analytics tool should have:
- Natural language processing: BI and citizen data scientists are the most to benefit from the linguistic capabilities of technology. NLP allows users to interact with the system in their natural language through text and even voice commands.
- Natural language generation: This empowers BI tools with augmented analytics capabilities to narrate results in an interactive way to help users comprehend the complexities behind the data.
- Recommendation: To counter any confirmation bias, errors and improve the effectiveness of the BI tool, augmented analytics system should recommend
- most suitable visuals for specific data,
- how to enrich data for better analysis and interpretations
- how to clean and prepare data for business use.
- Insight generation: Augmented analytics tools should be able to provide insights free from bias and achieve results coherent with the hypotheses. It is important for these algorithms to describe the data, identify key performance drivers, and elicit the segments/factors that influence the outcome. The tools must also identify the outliers that might behave differently from the anticipated results.
- Prediction: Augmented analytics tools should be able to generate forecasts and trends, identify statistical outliers and clusters with convenience. These processes involve using algorithms to train predictive models which are developed based on several business variables like churn, attrition and customer behavior.
Integrating augmenting analytics with artificial intelligence and natural language processing are key elements to improve the user experience across the data analytics value stream. Processes of data ingestion, insight discovery, understanding correlations in data, and user interacting become streamlined and effective.
Data is growing at an unprecedented rate with countless IoT devices and users are creating new digital footprints every moment. With data being captured and processed in a myriad of complex ways, powerful and robust analytical systems backed by AI are much needed to tap into the potential that lies with data.
Companies will need an augmented analytics platform to make sense from a large resource of data and share their findings across the entire organization with ease. Augmented analytics systems are essential to automate several key aspects of insight generation, increase the productivity of data scientists, improve data governance and accuracy, and reduce costs.
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.