AI is expected to start managing data itself through self-organizing data management capabilities that will accommodate evolving needs of data integration, cleansing, organization, storage and access.
Gone are the days when data analytics was only used as a tool to measure success. It is now seamlessly woven into the process itself thus contributing to delivering excellence. Businesses that have aced the use of data analytics are reaping the rewards of reading the signs right and actioning them in time.
While BFSI continues to be the largest adopter of analytical services, other industries have also seen good adoption and continue to gain further traction. The reasons for increased adoption include growing data volumes with improved Customer Experience (CX), personalization, operational efficiency, cost optimization, regulatory compliance and opening up of new revenue streams through data monetization.
Cloud services and modern Business Intelligence (BI) tools that have catalyzed analytics adoption across industries are also witnessing consolidation. Tableau has been acquired by Salesforce, while MSFT offers the full stack from Azure cloud services to Power BI. Data management and governance are now gaining increasing focus across industries and there is rising demand for agile cloud-based approaches for building simple, scalable, flexible data architectures.
Artificial Intelligence and Machine Learning have become pervasive across all domains. Their usage will further increase through hyper-automation by combining augmented analytics, IoT, RPA into cybersecurity systems, corporates systems, smart homes and cities. Further, AI is expected to start managing data itself through self-organizing data management capabilities that will accommodate evolving needs of data integration, cleansing, organization, storage and access.
The COVID-19 pandemic has further accelerated the need for businesses to be able to adapt to changing customer consumption patterns and business scenarios. The pandemic-induced disruption in supply chains has led to reconfiguration and greater focus on mitigating supplier risks.
Let’s take a look at some of the emerging trends in this space that can help businesses stay ahead in the game.
Augmented Analytics Augmented analytics is an approach that relies on Machine Learning (ML) and Natural Language Processing (NLP) to augment existing methods used by businesses to obtain actionable insights more easily. Businesses have been exploring the potential of augmented analytics to enhance existing data analytics processes, and changing the way businesses develop, consume and share analytics content. Augmented analytics can handle large data sets, and even scrub and parse raw data in a cost-effective and timely manner. This also means that data scientists can spend a majority of their time in delivering value to the business by looking for actionable insights.
There is a marked shift in demand from offline channels to online necessitating the need for companies to have a finger on the customer’s pulse at all times. With digitalization turning the heat on customer acquisition and retention sharply in the face of stiff competition, companies are increasingly looking at augmented analytics to reduce servicing costs and driving up automation and self-service.
Explainable AI is essentially understanding the rationale behind how an algorithm arrives at its recommendation or decision in the first place. This is not as easy as it sounds, as most AI models cannot explain how they get to a specific decision. Explainable AI could be particularly useful if a business suffers negatively after making a decision based on AI. At a time when even a minor error can result in erosion of market share and customer base, explainable AI can hold businesses in good stead and prevent costly mistakes.
As AI becomes more mainstream in the industry, Explainable AI helps business understand the relationship between decisions and outcomes better, further increasing trust on AI.
Pervasiveness of cloud
Cloud adoption has become crucial for enterprise data and analytics strategy as they offer scalability, reliability, continuity and cost efficiency. Consumption of public cloud services was growing rapidly well before the pandemic but the pandemic has further accelerated the adoption of cloud services. Cloud based AI is one of the top categories of cloud adoption with AI driven offerings like image recognition, language processing and recommendation engines becoming accessible to users. Further, hyperscale cloud vendors already offer integrated data to insights offerings within their service portfolio, reinforcing the pervasiveness of cloud.
Graph Analytics Graph analytics is used to analyze the relationships between different organizations, objects, people, transactions or even nodes in a network. By doing this, enterprises can check the extent of correlation between different entities, and how often they engage with one another, and whether or not there are additional entities that alter this dynamic. One of the key areas for the use of graph analytics is within social media networks. Graph analytics helps a business that is delving into data to find out more about a specific relationship. Natural language processing and generation, conversational BI, and social media analysis are becoming even more important than ever to triangulate accurate data about customers from all sources.
Unification of data and analytics
A convergence of analytics and the spectrum of data services is gathering pace under broader data and analytics governance initiatives in the industry. Consequently, service providers are also unifying data management, BI and analytics capabilities in a modularized umbrella offering.
These offerings enable organizations to have end to end support from consultatively developing data and analytics strategy to implementation plans, implement cloud-based solutions and run a data driven decision making business powered by AI/ML.
Decision making in real time
Companies adopting analytics, AI and cloud are moving towards consumption of continuous intelligence. Continuous intelligence combines real time analytics with business operations to recommend next actions. The increasing prevalence of actionable data to extract actionable insights, improved data accessibility through unified data sources and emergence of cleaner data through automated data cleansing is empowering adoption of continuous intelligence to optimize decision making and improve customer support.