Not so long ago, businesses evolved, because they were staring at impending failure. However, today, companies look at continuous evolution, for growth and sustenance and much of this can be attributed to the transcendence of IT’s role in an organization, from a mere enabler to a strategic differentiator.
Such has been the speed at which technology has facilitated a power shift towards a customer centric approach that all organizations, big or small, suddenly find themselves in some stage of digital transformation. Research suggests that close to 67 percent of the CEOs agree to having assimilated digital transformation as a major corporate strategy.
Past few years have seen digital initiatives like customer experience, business process transformation and data driven business models develop significant traction in board rooms across the world and now the momentum has shifted to enablement of these initiatives. Information management systems will be tasked to provide the digital readiness for the evolving digital enterprise landscape at the core, at the edge and at the link.
At the core, customers are looking for optimization. Adoption of open source and introduction of high performing layers in the IM ecosystem, will deliver both speed and cost benefits. At the edge, companies are aiming to leverage real time ingestions, end-to-end security & privacy as well as data collection automation. At the link, which is the interface layer between the core and the edge, the focus is on data management and exchange synchronization between core and edge applications.
If we consider the Data-Information-Insights value chain, information management systems sit right at the heart of it and has been continuously integrating both backwards into data systems as well as forward into the insights systems to keep pace with trends.
This year is going to be no different:
Traction towards flexible systems
Companies will continue to look to integrate data sources. With the variety of data, it is imperative that the information management systems are compatible both with the upstream and downstream data technologies. With increased social media and digital activities, unstructured processing technologies like Entity Extraction, Concept Extraction, Sentiment Analysis, NLP, Ontology etc. will witness an upswing.
Rise of the Real Time Data Enterprise
With the rising trend of ‘device mesh’, the speed at which data comes in, is getting further accelerated, while the batch times for data integration to match digital speed are shrinking. Also, digital applications based on Sensors, iBeacons and Customer Mobile devices stream data continuously demanding contextual action to be delivered to the edge devices, driven by analytics based on historical information. The business complexities also call for faster on-Boarding of new analysis capabilities in an agile fashion using agile oriented development techniques and reduce overall cost of delivery of a feed onboarding.
Data Gravity Shifts to Cloud
The marketplace has observed increasing proportion of Software as a Service (SaaS) applications in the enterprise, compared to on premise applications. With the hype around Internet of Things (IoT) translating into substance, the push for infrastructure to ride the digital wave will get stronger. Companies are re-imagining business models through variable boundary-less infrastructure. Digital applications (e.g. IOT) are increasingly being provisioned on a Platform as a Service (PaaS) platform in the cloud. Data Access, Synchronization, & Migration has gone beyond the enterprise firewall and SaaS data model changes happen at a much faster rate pushing new data elements to warehouse rapidly.
Data Security is Paramount
Increased digitization means increased risk of security breach by the ever expanding ‘hacker industry’. Companies risk losing critical data to untrusted sources if security and privacy governance around digital applications are unchecked. The companies, wary of the compliance and performance requirements, will drive the need for archival of both structured and unstructured data. A peripheral security set up will need to be augmented with systems that are capable of proactive detection and response to security threats.
In order to build the required trust between data and users, information management systems will focus on data privacy and use of critical attributes in digital applications to manage data holistically across core and edge layers of the enterprise.
With increased volumes of data, it has become critical for SMEs to analyze data structures and extract Metadata. Automated metadata extraction, based on ontology, is becoming an imperative to maintain the standards of data quality. Companies will look forward to leverage machine learning technologies to automate extraction, Data Integration and Data Quality. Automation will not only help save effort and time for but also scale up the systems to meet the needs of digitization.
The ability to acquire and store data is no longer the focus area, instead, the question really is how well we manage the data and what we do with it. With more devices connecting to the data mesh and explosion of data sources, we have information of everything. However, the differentiation really lies in the effectiveness of information management systems that enable the data to insights journey for companies and helps derive actionable insights for tangible business benefits.