A Gartner study revealed that by 2025, data stories are likely to be the most widespread method of consuming analytics, and 75% of data stories will be automatically generated through augmented analytics. 
Experts in the industry opine that while there is no dearth of data in the world today (in fact, by 2025, the amount of data generated globally, on a day-to-day basis, is estimated to reach up to 463 exabytes)  , what most firms continue to struggle with is making sense of this data. And this gap in optimum data consumption has created the need for an easy, scalable consumption model, i.e., data stories.
Many organizations find themselves unable to translate analyses into actionable insights that power informed decision-making. Moreover, businesses are often unable to efficiently leverage the data that they already have at their disposal.
Reports indicate that 55% of all data collected by companies is dark data, i.e., “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing) . ” This dark data generally includes two kinds of data categories:
• Data that the company knows it has captured but does not know how to use
• Data that companies do not know that they are in possession of
To facilitate the swift and holistic utilization of data, it is important for firms to move from the traditional dashboard setting toward a decision board, i.e., a more dynamic, AI & ML-powered, augmented analytics setup.
Decision boards do not replace dashboards but rather improve on existing setups. They enhance how users interact with data insights and improve ease in the generation of data stories, catered to the needs and KPIs of the end user. Instead of following a laid out, linear path of data analysis comprehension, with decision boards, each user gains access to data points and data stories that are most relevant to their business function. Furthermore, decision boards can also be tailored to cater to the abstractness of questions posed by end users when they look to comprehend data.
With augmented analytics, repetitive tasks such as data preparation, mining data insights, creating an ML model whose accuracy increases over time, and so on, can be automated. This automation frees up analysts and IT teams from manual data and analyses efforts, enabling them to focus on improving data consumption practices across the organization.
Here is an overview of how user experience will transform with the help of augmented analytics:
In a nutshell, these changes indicate a marked move away from time spent in exploring and experimenting with data changes by clicking on dashboard channels and trying to understand and discern patterns and trends. Instead, users can directly interact with an AI-powered ML model, with pointed queries pertaining to trends, analysis of a selected scenario or segment.
Additionally, creating a provision for digital assistants and chatbots will only increase ease of access to and comprehension of these data stories.
With this, it becomes easier to mine data stories and share them with executive teams for better decision-making for pivotal operations. This will help improve firms’ agility in responding to market changes, by reducing the time taken by teams to create and present needed reports/data points.
While augmented analytics is expected to be widely adopted by 2022, studies show that only 10% of companies will be able to derive maximum benefits from the same. And while companies must look at adapting the technology wherever possible, this road is not without challenges.
The lack of requisite technological setups, budget constraints, lack of robust data mining and collation setups, sub-par data, employee distrust in change, and the move to automation are likely to hinder optimal augmented analytics adoption.
Companies need to create awareness that increased adoption of automation in augmented analytics does not indicate a reduced need for human resources. On the contrary, even if automation generates data stories for relevant contexts, it is human talent that can best comprehend data and translate it into contextualized, customized insights that improve with every iteration.
Here’s how firms can prepare for the same:
• Consistent analyses of existing tools and analytics setups. By comparing them with latest industry innovations and standards, businesses can consistently ensure that the data stories generated are top-notch and offer improved results as compared to the conventional dashboard.
• Ensuring that learnings are shared across the company. Evangelization, along with accountability, should be the focus, so that practices can be standardized, enabling swift identification of opportunities, deployments of projects, and mitigation of problems.
• To improve user relation with data, companies can also showcase internal success stories of augmented analytics adoption and utilize the same to encourage behavioral change.
While dashboards required a certain level of data literacy among consumers, decision boards are easier to work with as they are customized for end users.
The future is decision boards. To enable the efficient utilization of decision boards, companies must consistently innovate to ensure user experiences are optimized. The more the ease of data consumption in an organization, the more efficient is its analytics adoption.