Data Needs to Treated as Strategic Asset for Better Management
Data analytics is no more of competitive advantage but is a pressing need for core businesses to make real-time decisions and make changes in the economic landscape. Traditionally, companies have focused on collecting and visualizing data for the next phase of the data analytics journey to transform and redefine their organizational goals. Like a product development team, data teams will adopt practices like storing, moving, managing, and visualizing data which comes as a challenge when companies want to centralize it. Not only for companies working in data engineering, but for every company data is a new product, as they try to reinvigorate, try to improve retention, and deliver better results.
For many companies, the explosion of data is net positive and they are trying to adopt new data stacks in an earlier phase of their lifecycle with affordable and easy tools which include cloud data warehouse, BI tools, etc. Stepping into 2022 comes as a challenge in tooling for data processing, data migration, data integration, and movement especially when organizations are betting on trends like data observability, data governance and auditing, data discovery and visibility. In a survey of 150 enterprise tech leaders done by Matillion, data maintenance and migration costs around $43.5 million per year, hence enterprises struggle to turn data into useful insight.
Once organizations are set to look at data as a strategic asset and reduce the time taken for manual processes of data management and architecture, they would be able to deliver firepower data projects. To come to an optimal data-driven state, organizations will need access to reliable, timely, and comprehensive data. Role of data engineers will seep out of its current silos. Data documentation and cataloging will be more concentrated for less cost of data storage.
What Challenges can Data Engineers Face in Data Management and Integration?
For digitally engaged customers and clients, knowing all about data and using it effectively will be the only possible solution. Enterprises will need to extract it from sources, apply insights over more channels, and perform all of these continuously which requires hercules efforts. For such data governance and management practices following challenges needs to be addressed:
1. Challenges Related to Data Sources: Data keeps growing in volume and so is the difficulty in managing the data related operations and the cost to turn it into useful insight. Not only volume of data but growing data sources also impact organization’s ability to make data useful.
2. Challenges Related to Data Quality: More than storing petabytes of data, storing them in a way enterprises can analyze, and using them in a clean, secure and governed manner is a challenge. Data management faces a major issue of questionable data quality and underlying structures. These two most frequent occurring data quality issues appear:
Automation and Workflow Orchestration will be Turnkey Solution for Addressing Challenges
As companies ingest more data, unstructured data becomes the norm. Data teams must be capable of leveraging their data catalog without a dedicated support team. What really needs to be revamped are: people and technology both, hence data engineering teams need to have a holistic approach for data product creation. Prioritizing the aspects of data strategy and pipeline that are under-developed is another way to improvise data management initiatives.
Vendors and companies nowadays are understanding that the needs of scale, speed, and varied use cases may also require a couple of databases. Data teams that invest in and take full advantage of the right resources, automation, and workflow orchestration will be better able to outperform competitors with data, and will be prepared for the ever-changing future of data use.
Techment Technology believes in having end-to-end data quality and removing data errors which are always customer-facing. Our people are more aligned in helping our customers by building solid mental models around data. For more conversation on data projects, get our free consultation.
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