Every enterprise has a mix of software solutions to address the various challenges they experience in their data management lifecycle. Data governance, data protection, and security, native storage and hybrid cloud management, data cataloging , data accessibility, reporting – are just some of the data challenges data stewards deal with daily.
DATAVERSITY® released the report Trends in Data Governance and Data Stewardship, based on an online survey that garnered responses primarily from Information and Data Governance specialists, Data and Information Architects, and Business Intelligence and Analytics professionals. Findings from the study revealed, among other things, that there’s still a lack of maturity in data management tools and absence of one, a central platform that addresses if not all, but at least 90% of the above challenges.
As we approach 2020, we asked leaders and users in the data management space to identify some of their biggest data challenges and describe how they are planning to approach data management challenges in the new year. Here, we are sharing some of the key takeaways of the survey we conducted.
Biggest Data Management Challenges?
1. Data Analytics and Reporting:
Collecting data and representing it to meet business decision-makers need remains the biggest challenge. Quite the opposite of what we thought, most data stewards we interviewed were quite confident about finding data they need in minutes if not seconds. However, the challenge starts from there.
It is challenging to represent a high volume of data in a way that is readily useable for business decision-makers. The problem gets more complicated when advance businesses in fast-paced sectors such as retail, travel, food, and packaging and insurance would like this data quicky for artificial intelligence or process automation.
2. Data Privacy and Security:
In the post GDPR world, data security, compliance, and data privacy remains as quite a significant concern. We asked senior data managers if they felt their existing data security and privacy measures were fool-proof, and only 3% were confident that their data protection measures were fit for purpose. The stats below collected by IBM reflect the same.
The stats above clearly show the gap between data requirements and how ready we feel to use if for business benefit.
3. Data Quality:
Data modelling and the success of AI is dependent on the quality of the data. Although organisations are keen to leverage the full potential of their data, poor quality of data holds them back. The results of data modelling are only as accurate and useable as the quality of data. There is no surprise that senior leadership continues to have improving data quality and governance on their list of priorities for 2020.
Moving forward with data-driven strategies
There is a split between businesses that take a defensive stance towards data lifecycle management and most of their strategies are built around pain avoidance. On the other hand, 2020 will see the emergence of data-driven businesses who will be better placed to transform their businesses, achieve a competitive advantage and serve their customers better.
Start your data management strategy with a clear view of your data with The DOT groups DETCT – Data analysis service. DETCT analyses your data infrastructure, operational data, and business data usage and gives you a clear dashboard of insights that would be valuable to develop your 2020 data strategy. Book your DETCT investigation here.