How to start your data modernization journey
Getting value from data is not something that can be done in a (data) snap. The journey is often long and tumultuous. According to a recent survey, 99 percent of companies recognize that data is crucial for success; however, 97 percent face challenges in using data effectively.
The spread of data sources, volume, and too few resources prevent organizations effectively managing the growing demand for trusted data at the speed required by businesses. And that means little data -- if any -- can be relied upon to make critical business decisions. But there are simple key steps that every company can make to help them ensure a sustainable data management practice that will enable them to accelerate time to value.
Make cloud migration simple
Cloud migration is becoming increasingly common among businesses, and more and more organizations are adopting cloud strategies to improve their agility, scalability and cost-efficiency.
To maximize the value of cloud infrastructure and optimise cloud data management, organizations should have flexible options for the ongoing management of whatever solution they have in place, whether it is self-hosted, managed or SaaS. The data management solution should be scalable and extensible, cloud-independent, or hybrid to support a variety of deployment architectures and applications.
To facilitate cloud migration, data movements should also be simplified. Advanced capabilities to automate task scheduling and orchestration management, for example, can help smooth operations, schedule tasks and ensure the on-time delivery of data needed for reporting purposes.
With the increasing numbers of data sources and systems, modernizing a data management strategy should also be based on powerful and extended connectivity to enable better collaboration and more efficient workflows. This also ensures data accuracy and consistency across the different systems to improve decision-making, increased productivity and overall a better understanding of business operations.
An adequate connectivity should include business-critical applications support such as SAP S/4 Hanna and Workday. Data-driven decisions can’t be possible without the right connection to new platforms such as TikTok, Pinterest or Google Analytics 4 to support measurement of your marketing success. Organizations can easily gain insights into their customers' preferences and behavior to better personalize marketing messages and deliver a better experience, while optimizing marketing spend and improving the return on investment.
Lastly, organizations have been adopting modern cloud databases to better scale horizontally and vertically, optimizing resources and increasing performance. Data integration connectivity should be able to connect and query modern cloud databases like Amazon Keyspaces, Azure Cosmos DB or Neo4j Aura to support organizations’ evolving architectures.
Democratize data quality
To maximize efficiency across an organization, everybody -- not just technical users -- need to be able to participate in organizational data management. Gone is the time when maintaining trustworthy data was simply an IT function. Data is the whole company’s priority and shared responsibility. No central department, whether it’s IT, compliance or the Chief Data Officer’s team, can magically cleanse and qualify all organizational data. It’s better to delegate some data quality operations to business users because they’re the data owners. Business users can then become data stewards and play an active role in the whole data management process. It’s only by moving from a centralized model to a more collaborative role that you will succeed in your modern data strategy.
When building a modern data management stack, organizations need to discuss with business users to understand the objectives and data-related issues to better empower internal stakeholders with data. Self-service applications including data observability capabilities give a holistic view of organizational data and allow data professionals to monitor the effectiveness of data programs and quality interventions. This means they can easily discover new datasets and uncover data blind spots.
Data professionals should be able to quickly identify how qualified trust aspects of data have changed over time and which quality interventions have impacted the data, making it easy to stay on top of data drifts. To act on the issues sooner and take a proactive approach to data quality, data teams can also apply custom rules that bring more business context to measure data quality in a more business-relevant way than with standard technical metrics. Pre-existing rules increase productivity and efficiency as they can be designed once and reused anywhere across different applications.
The case of Vyaire Medical, a major respiratory player in the global healthcare ecosystem, is a proven expert in data-driven adaptability following a massive spike in demand for ventilator equipment in 2020. The company unified data from 12 ERP systems on a cloud-based enterprise data platform. By gaining confidence in the quality of data and analytics, Vyaire Medical achieved a 100x in production and cut the failure rate by 2x. Data allowed them to predict trends, develop informed solutions, understand where there was flexibility, and take action to remain competitive.
A modern data management strategy should meet key business requirements including architecture scalability, security and compliance but also business value.
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Felipe Henao Brand is Senior Product Manager, Talend