The average lifespan of an S&P500 company is now less than 20 years – down from 60 years in the 1950s. If large organizations are going to survive, they need to radically rethink strategies and business models.
Grocery stores are launching banks. E-commerce businesses are building brick and mortar locations. Amid all the disruption, the difference between longevity and irrelevance is being defined by how effectively companies use data.
The volume of digital information stored on planet Earth will hit 44 zettabytes this year — 40 times more bytes than there are stars in the observable universe.
When it comes to data generation, we’ve exceeded expectations. It’s the process of getting the data to the people who need it, when they need it, that businesses still struggle with.
Data exists in too many places, in inconsistent taxonomies, in huge volumes, across operational and analytical systems.
That leaves too many organizations stuck basing their strategic decisions on information that is at best dated, at worst unreliable. It’s a shaky approach to analysis and planning that won’t allow them to release the latent value locked-up in the reams of enterprise data accumulated every day.
The last century’s IT mantra was to get the right data to the right person at the right time. Now it’s about getting the right data to the right person at the right time – in the right way.
Data needs to be correct and in-line with regulatory, statutory, and organizational governance. To move at the speed of digital, this all has to happen without human intervention.
But how? If you consider how much enterprise data comes from sources outside the business – from the vendors of vendors and customers of customers – it’s no wonder organizations struggle with discovering, understanding, and trusting the quality of their data.
Legacy systems hobble progress by imposing inefficient processes. Too many lack the agility to deliver time-sensitive data insights quickly—an absolute requirement for staying competitive.
Stitching together new point products to achieve end-to-end management is an alternative, but that adds complexity as well as cost. It can take up to 10 separate solutions, and the disjointed nature of the final solution can trap you in a perpetual DIY mode, endlessly managing delays, changing roadmaps, and rising costs.
To overcome these barriers, organizations are investing in cloud data warehouses, data lakes and, more recently, data lakehouses, designed to store, update, and retrieve highly structured and curated data for data-driven decision making.
But even the lakehouse model faces challenges. It needs enterprise-scale data integration, data quality, and metadata management to deliver on its promise. Without the capability to govern data by managing discovery, cleansing, integration, protection, and reporting across all environments, lakehouse initiatives alone are destined to fail.
The stubborn resilience of manual processes is one of the most significant barriers to success. Relying on them to build a data pipeline limits scalability and creates unnecessary bottlenecks in execution. Manual ingestion and transformation of data, for example, can be a complex multi-step process that creates inconsistent, non-repeatable results.
To deliver high-quality, actionable data to the business quickly, you need an AI-driven data management solution that offers a complete view of where all your critical data resides across different silos, cloud repositories, applications, and regions.
With the growth in data volumes, it is nearly impossible to spot data quality issues on a manual basis. In contrast, using AI to detect signals of incomplete and inconsistent data using automated business rules can have a dramatic impact on the reliability of analytics.
A data-powered transformation is underway, changing the way organizations leverage business information to make crucial strategic and operational decisions.
AI-driven data management addresses the issues around data governance, volume and complexity that just keep growing. Building an environment that can deliver trusted, timely, and compliant information – to the right people, at the right time, in the right way – is the crucial last step in unlocking data’s potential.
Powered by meta-driven machine learning and automation, this new model curates all critical data management processes, from ingestion to preparation and governance. It democratizes access, enabling stakeholders from across the business to conduct their own sophisticated analyses from a single source of truth.
Connecting all the dots requires a vendor that’s invested massively in R&D to create solutions that can continually read and monitor all the disparate repositories, point solutions, IoT devices, supply chain feeds, and data formats held on-premises and in multi-cloud environments. Business needs a single, robust, intelligent catalogue that tags and applies governance rules automatically.
With AI-powered and cloud-native data management from Informatica and Capgemini, you can finally unleash the power of your data—across multi-cloud environments and for information living inside the enterprise and out.
A global leader in consulting, technology services and digital transformation, Capgemini is at the forefront of innovation to address the entire breadth of clients’ opportunities in the evolving world of cloud, digital and platforms. Building on its strong 50-year heritage and deep industry-specific expertise, Capgemini enables organizations to realize their business ambitions through an array of services from strategy to operations. Capgemini is driven by the conviction that the business value of technology comes from and through people. It is a multicultural company of almost 220,000 team members in more than 40 countries. The Group reported 2019 global revenues of EUR 14.1 billion.
Learn more at www.Capgemini.com/AIEngineering