It won’t be possible to give an overview of data management trends 2020 without conducting an analysis of the clients and the projects we worked on in the past year. In 2019, Data Ladder worked with a number of leading enterprises including government organizations and public sector institutions to optimize their data with our data cleansing solution.
After working with a number of institutions, organizations and enterprise-level businesses, we have gained some crucial insights on data management trends that are changing the industry.
From our experience of the past year, here’s our overview of data management trends 2020.
Data Quality at the Top of Data Management Trends 2020
It goes without saying that poor data quality is costing organizations millions of dollars annually. In fact, according to Gartner, ‘the average financial impact of poor data quality on organizations is $9.7 million per year.’ According to IBM, ‘Businesses in the U.S alone lose $3.1 trillion due to poor data quality.’
These aren’t just fancy statistics. Almost every business we’ve worked with reported some form of financial loss because of bad data – but it’s not just financial loss that businesses worry about. Bad data quality affects decision-making, business objectives, logistics, customer loyalty, brand reputation and a lot more. For government organizations, bad data results in inaccurate statistical analysis, incorrect reporting and in some cases, losing a much-needed grant.
Fortunately, most businesses now realize that in the modern world, data is more valuable than gold. It is the very lifeline of an organization. This realization coupled with increasing customer demand for digital services has made data management the central focus – and data quality at the heart of this initiative.
For 2020, businesses will make various efforts to streamline their data in order to identify opportunities, meet with rising demands and ensure optimized business operations. In order to manage data though, businesses will need to start with cleaning data, ensuring that problems such as duplicates, inaccurate or incomplete data, and poor standards are nipped in the bud before working on any other aspect of data management.
Increasing Reliance on Data Analysis
In 2020, as we enter an age of IoT devices, interconnected systems and platforms leading to unprecedented levels of data growth, businesses will rely on data analysis to derive key business insights. Organizations will need to implement various methodologies to interpret intricate data values or information – from using visualization tools to business intelligence software solutions, a range of systems will be used to make sense of data.
In industries like Education and Healthcare, data analysis enables data-driven decision making. Take for example the case of a major state using data analysis to address disciplinary problems and evaluate the overall student performance of schools in the district. Data analysis at such intricate levels requires that data profiles be optimized, standards are set & duplicates are removed before an accurate analysis can be performed.
Integration of Databases for Real-time Cleansing
Another crucial data management trend is real-time data cleansing – meaning data needs to be up-to-date and always accessible. Real-time data processing involves a continual input, process, and output of data where detection of errors as well as its correction must be attempted within seconds or minutes. To achieve this, databases must be connected with a data cleansing tool. Consumer-facing businesses such as retail stores, banks, ATMs, etc need real-time cleansing options to ensure that data quality does not suffer.
Users of DataMatch Enterprise have the benefit of integrating their data systems with the DME platform. There is support for over 150 platforms, empowering businesses with the opportunity to manage their data in real-time without moving away from their central platform. From Facebook to Zoho, Oracle to QuickBooks; almost every platform can be integrated within the DataMatch Enterprise platform, enabling real-time data updates and maintenance.
Increase in Data Standardization
To derive intelligence from data, it needs to be standardized – meaning, all fields should follow a defined standard for recording information. For example, a company may want the names of all states to be written in its complete form instead of in abbreviations – Washington instead of WA, New York instead of NY or NYC and so on. Similarly, phone numbers must be written with city codes instead of just the number. Such inaccuracies and lack of standardization result in poor data quality, causing an unnecessary workload on teams to rectify and make sense of data before implementing a data initiative.
Data standardization is an even greater need in 2020 as IoT is paired with Artificial Intelligence, creating metadata, the foundation of training data for machine learning models. Chatbots, AI predictions, smarter software, and applications are AI systems that require clean and clear data to function well. Standardization of data has a direct impact on AI tools’ performance.
Digital Transformation & Data Management
In 2019, businesses realized the importance of digital transformation – in 2020, businesses will begin implementing the transformation process. Many will move to faster, better, more reliable ERP systems. Many will adopt AI tools and technology. Many will move to cloud data storage. Most businesses we’ve worked with have a digital transformation plan in place and are ramping up their processes to meet with rising customer as well as regulatory compliance.
For effective digital transformation to take place, it is mandatory that data management is at the forefront of all initiatives. When your lists are clean, it’s easier to navigate the transformation process. In fact, every successful digital transformation has a data strategy in place along with other essentials as customer experience and security as its foundation.
Digital transformation in 2020 is not only limited to the private sector, but the government sector has also taken a keen interest in updating their systems and implementing digital transformation. For government and public organizations, the need to have a data strategy at the heart of a digital transformation strategy is a crucial first step in preparing for the new tech age.
Conclusion
2020 is the age of digital transformation. Businesses know they have no choice but to upgrade their systems and platforms to meet with consumer expectations, however, data management remains a tricky area. As businesses are implementing migration processes, or making use of data to obtain key insights, they are realizing the flaws of their data.
What used to be a component pushed in the back-burner due to limited resources is now one of the most crucial areas of improvement. Data is the new gold. Data is the lifeline of an organization, but without clean data, you’re nowhere near achieving key business goals.
