One of New York City’s well-known nicknames is The City That Never Sleeps. However, now it is the digital world that has connected an entire globe, not just a city that never sleeps. The digital world keeps us connected 24/7/365 in our lives. Every second, every person across all parts of the world is interacting with the digital world and creating data in a variety of ways.
With 3.7 billion humans currently using the internet every day, we have created nearly 90% of the world’s data in the last two years alone. According to Statista, data creation will be over 180 zettabytes by 2025, which will be 119 zettabytes more than in 2020. For reference, a zettabyte is equal to trillion gigabytes – and one gigabyte can translate to 350,000 emails.
Data has and will become even more the lifeblood of an organization. The ability to get insights from this data explosion as quickly as possible will be critical to help evolve a company’s business and operation models, as highlighted by the pandemic.
To handle this data explosion, our approach and use of business intelligence (BI) is undergoing one of the most dramatic & innovative shifts that we have seen in the evolution of BI. Let’s take a brief review of the history and current challenges of business intelligence:
- First was business intelligence 1.0, which was led by semantic layer-based platforms. Requests from business users for information and data were provided to IT Analysts, who would run reports that could take days, weeks, or months to deliver depending on complexity. The final report was heavily numbersoriented, and insights could take weeks to months to be delivered.
- Then came business intelligence 2.0, which saw the emergence of self-service analytics designed for business users to generate reports and data by using interactive interfaces that allowed for click-drag-drop functionality. Data visualization tools became heavily popular as they allowed for insights to be derived in days and hours.
While business intelligence 2.0 has greatly improved the level of analytics and insight, it still faces mounting challenges in organizations highlighted by:
- Lack of Insight Generation – Most dashboards deliver only a fraction of the insight needed to monitor and maintain organizational needs. Due to the massive amount of data explosion over the last 10 years, data discovery is now needed for most insight.
- Adaptability and feedback – Less than a third of a dashboard user base will log in to view company dashboards. Most of this user base will probably only use it once a month. Without adoption, we lack the necessary feedback to continuously improve business knowledge and improve insight intelligence.
- Democratization of data – Unfortunately, most organizations cannot provide dashboards to all users, as it would be costly. This limits organizations’ ability to empower all employees to make data-driven decisions.
- Data explosion and underutilization – It is estimated that only 0.5% of data is being analyzed and used. Data collection continues to increase by 40-50% every few years, leaving a substantial amount of data unlevered. This unearthed data could be assisting in helping new & lost revenue opportunities, identifying quality issues, and so much more.
These challenges have now led us to the next evolution of BI.
- Business intelligence 3.0, which is being highlighted by a predominately app-centric approach to BI, with users having multi-device interfaces and helping to simplify data complexities democratize data to enable collaboration, social empowerment, and provide insights in real-time to all business users. The stage is being highlighted by augmented analytics to help achieve these initiatives.
Augmented analytics is defined by Gartner as an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist.
The field of Augmented Analytics is helping to achieve the following:
- Augmented data preparation – uses machine learning automation to augment data profiling and data quality, harmonization, modeling, manipulation, enrichment, metadata development, and cataloging.
- Augmented data discovery – enables business people and citizen data scientists to use machine learning to automatically find, visualize, and narrate relevant findings without having to build models or write code.
- Augmented data science and machine learning – automates key aspects of advanced analytic modeling such as; feature selection. This reduces the requirement for specialized skills to generate, operationalize and, manage an advanced analytics model.
These advancements are helping us achieve the primary goal of BI, which is to create value from data in order to obtain insights that can be acted upon by a company. The two primary types of insights that a company wants to obtain are:
- Increasing revenues – Examples of this scenario include identifying the factors that are causing MQLs to SQLS and then further converting to a successful sale while always considering the other side, what factors are preventing a successful sale, for example, are sales personnel reaching out in time and are they using the most desired communication methods by clients.
- Eliminating costs or helping to create cost efficiencies. An example of this scenario includes automating employees in order to perform higher value-added work for your company.
Augmented analytics will be crucial for delivering unbiased insights, recommendations, and impartial contextual awareness. It will transform how users interact with data and how they consume and act on insights with conversational analytics. This emerging shift is enabling people to generate queries, explore data, and receive and act on insights in natural language (voice or text) via mobile devices and personal assistants.
Insights in natural language are helping to create a more meaningful impact and impression in an individual’s mind.
The most innovative and successful Business-To-Consumer (B2C) applications have been using advancements in augmented analytics to provide personalized and contextualize insights over the last few years. These companies have mastered the retention and analysis of their customer data and have effectively used such methods to create value for themselves and their customers. Let us look at the following companies:
- Amazon “People who bought this product also bought these items”: A recommendation system harnessing the massive amount of personal purchasing data in Amazon’s ecosystem. This recommendation system has become a personalized shopping assistant that gives you the best suggestions for complementary items that you may want to purchase.
- Uber “Relax and enjoy a ride for the holidays”: Simple and effective nudges to push users to open the app and book a ride. Once in the app, users see a variety of machine learning models involved in the surge pricing that is beneficial for drivers and riders.
- Netflix “We just added a TV Show you might like”: Another recommendation system analyzing the viewing patterns of people with similar profiles and previous viewing experiences. In addition, the user feedback mechanism helps to improve the recommendation algorithm.
- Nike Fitness “Take a brisk 15-minute walk to close your exercise ring”: The use of nudging principles to help drive intended behavior and meet your stated goals. These types of insights prove how effective, simple and easy-to-digest insights can be to end-users.
- Waze “Best Route, Usual Traffic – ETA 45 minutes to Destination”: An app whose maps and navigation are powered by users. The more people are driving with the Waze app open, the better the navigation experience creating live feedback and data collection process. The real-time information shared from your device translates to traffic conditions and road updates i.e., traffic, accidents, blocked roads, hazardous items on the road, etc.
In essence, each of these apps has created a personal, digital assistant for users to help them drive engagement and nudge behavioral actions by providing personal and contextualized insights – all brought to you by the power of augmented analytics.
As our B2C experience and expectations have evolved, expect this shift to make its way to our employers and our work lives. Work still accounts for a significant portion of our time, and companies should look to invest in augmented analytics in order to provide employees with a personal digital assistant to help provide the right insights at the right time to help them to perform better, make data-driven decisions, help employees to hone skills and preparing them for future opportunities in the company.
