蘑菇短视频

蘑菇短视频听Head of AI,听,听recently joined a panel at Data Management Summit USA Virtual discussing the science of data discovery and the art of implementation of business value. This panel sought to unpack the greatest data discovery challenges that have surfaced as a result of increased data volumes,听and financial institutions making more use of cloud environments.听听

Fiona was joined by听,听,听, and .听

The听panellists听focused on:听

• The听merits of听leveraging external data management tools听
• What exactly a robust DQ program or tool can do听for听your data听
• The importance of creating a golden record or single version of the truth, and听
• Implementing听applications of AI/ML to automate some of the data discovery processes听to听generate better quality data across the enterprise.听

Key takeaways from the discussions were:听

1. There are many benefits of data discovery听

At听蘑菇短视频, Fiona pointed out that we often see a spectrum of capabilities in terms of where institutions are in their maturity and data journey.听Fiona stated that听data discovery helps at all stages of implementing a Data Governance Framework, uncovering relationships and making use of things like metadata听catalogs, but that听at all times听it is vital to understand the importance of embedding context听to aid downstream applications and use cases.听

Fiona said that听it is vital to consider where you are with your Data Governance Framework, asking yourself the question: are you at the start of your journey,听or are you well established within it?听Do you have ownership and responsibility of the data, an understanding of security and accessibility; do you fully understand the regulations, legal implications and security of your data?听听

Data discovery is a key tool to be听used at all times听in these processes, enriching data with context that will help in the long run, whether mature or just starting out in Data Governance.听

2. Leading organisations are embedding a听culture of data responsibility and understanding within an organisation.听听

These organisations听have听C-Suite executives right听through听to data engineers on the technical side听all engaged on their roles and requirements when it comes to data as a prized business asset.听

Firms looking to exploit this will need to focus on听skilling up and training because this area is constantly changing, regulations are updating and adapting. For听example,听Fiona mentioned the new AI regulations from the EU that are coming down the line. This will require keeping an eye on best practices.听听

3. Security is always moving!听

Security is always moving and keeping up to date has never been more important. Accountability comes from assigning those roles听and key responsibilities so that the key owner of this one single data state is known.听There are many policies around data privacy, ethics and security,听and of course quality so having a holistic view of those and then finishing off with best practices听that then highlights听well听in terms of having your standardised data model across a company.听One example is in听building data catalogues, where听it is important to categorise your data both in terms of risk and regulation.听

4. There is a听wide range of听tools听and solutions that can help with data discovery听

It depends on where you are on your data discovery听and听how much expertise and technology you have in house. Fiona stated there is a real range from data lineage and visualisation tools. There is also a lot of evidence that these tools are being augmented, for example, data lineage is being augmented with data quality metrics as it flows through an institution听and听technology to aid for example with metadata management.听听

The rise in 鈥榥o-code鈥櫶齪latforms, such as听蘑菇短视频鈥� Self-Service Data Quality,听helps听subject matter experts with things like measuring and monitoring their data quality, improving their data quality right through to things like graphs and graph analysis to uncover potential links and听relationships between data as systems get larger and more complex by nature.听

Fiona rounded off the discussion by stating听that it is extremely encouraging听to see us moving along in terms of augmenting the data discovery process with Machine Learning. This will听address those听more听manual,听time-consuming听tasks听that data stewards, analysts and business users end up saddled with.听听听

About Self-Service Data Quality听

Our no-code听platform allows users to create complex rule logic with a drag and drop interface with no specialist programming skills required.听蘑菇短视频听Self-Service Data Quality platform empowers business users to self-serve for high-quality data, saving time, reducing costs, and increasing profitability.听

In the highlight video below, you can watch Fiona discuss听these areas in greater depth.