Skip to Content

Why data needs to be hands-free


Advances like data orchestration and data lakes have made broader access easier – but many organizations still struggle to make widespread delivery of comprehensive, trusted data a practical reality.

Before analytics systems inherit it, data needs to meet regulatory, statutory, and governance requirements. It has to be correct, accurate, and properly catalogued with known lineage. All this should happen with minimal human intervention. Depending on a company’s existing data management infrastructure, that can present significant challenges.

Building a hands-free data pipeline

The world’s store of data is set to hit 35 zettabytes this year, and that Everest of information increasingly lives in a blend of cloud and on-premises environments. It’s not uncommon for half of enterprise data to come from outside sources, from IoT to the vendors of vendors and customers of customers.

With data volumes skyrocketing and the number of places it’s stored in growing, it’s no wonder organizations find it hard to discover, understand, and trust what’s in their systems – much less be prepared to share it widely.

Legacy on-premises systems hobble things further. Too many of them lack the agility to deliver time-sensitive data insights quickly – an absolute requirement for staying competitive.

To overcome these barriers, organizations are investing in cloud data warehouses, cloud data lakes and, more recently – cloud data lakehouses, designed to store, update, and retrieve highly structured and curated data, primarily for business analytics and 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 are destined to fail.

As businesses look to move their data to the cloud, hand-coding often comes up as a straightforward way to build the data pipeline. But hand-coding can create bottlenecks. It’s also a manual process, and its cost can go up as complexity increases.

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.

Automating the cloud data lakehouse

The stubborn resilience of manual processes is one of the biggest barriers to becoming a data-powered organization. Relying on them 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.

Getting rid of out-of-date processes can be a cultural as well as a technical challenge. Improving data literacy within the organization has to be part of the solution.

If they’re going to benefit from wider access to data, stakeholders need to understand how activities such as cataloguing and cleansing help ensure complete and accurate data, and how the accuracy of analytics changes the effectiveness of models and forecasts.

A marketer working with the wrong data could find themselves with a distorted picture about the customers they’re trying to target. That could lead to the development of ineffective campaign messages, and fewer products being sold.

With so many technological advances in system scalability and agility, ensuring that analytics systems inherit clean and compliant data operational systems that feed them without human intervention is now entirely do-able:

  • Automated data ingestion from known on-premises and multi-cloud data sources is a proven technical approach that adds agility, speed, and repeatability.
  • Automation also suits the fast iteration and flexibility requirements of agile development, because changes can be made very quickly with minimal risk of bugs.

Automation becomes even more vital when data quality is on the line. Problems that aren’t caught early during ingestion can cause broader downstream issues. That can dramatically affect business insights due to inaccuracies or inconsistencies between different data assets.

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.

Unlocking the power of trusted data

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.

AI-driven data management can help achieve this by unlocking the power of trusted data. By building a data environment that can reliably deliver trusted, timely, and compliant data to the right people at the right time, business can finally unleash the power of all that information in their systems.

Driven by machine learning and automation, this new model automates critical data management processes, from data ingestion to data preparation and data governance. It works across on-premises and cloud environments to accelerate the delivery of analytics insights to business leaders. Stakeholders can work with ever-greater volumes and varieties of data and turn them into trusted sources of high-value information for more data-driven decision making.

A data-powered transformation is underway that will change how organizations, employees, and customers think about, value, and engage with data.

With AI-powered and cloud-native data management from Informatica and Capgemini, you can leverage this trend to unleash the power of your cloud data warehouse, cloud data lake, and cloud lakehouse – for data living inside the enterprise and out.

For more info about how Capgemini and Informatica can help solve your business’ data management concerns, please reach out to the author, Danny Centen, Head of  AI and Data Engineering at Capgemini Australia & New Zealand.