Data culture is imperative to business success.
Organizations are changing out of necessity. The volume, variety, and velocity of data is exploding. Workforces are changing with turnover and restructuring, new subject matter experts, and a large percentage working from home. There are new compliance and privacy concerns as cyber-attacks continue to grow.
Alation strives to help people make better decisions based on evidence and data. Their vision is to empower a curious and rational world with a data culture that results in data-driven decision making. Core data intelligence capabilities include data search and discovery to find and understand data. Data literacy enables proper interpretation and analysis. Data governance ensures responsibility and authority.
The benefits of a data culture are compelling. Insights-driven businesses are 7X more likely to increase revenue and are 2.8X more likely to have double-digit growth.
The stakes are also high with fines of 20 million Euros or 4% of annual revenue (GDPR), $5 million fines for misleading financial reports (SOX), and incorrect decisions made with bad data or bad models.
More than three-quarters (78%) of organizations have a strategic data initiative and 86% have a C-level officer responsible for data. However, only 12% of companies are performing at a high level in terms of having an effective data culture.
Living catalogs enable you to learn quickly about products sold on the web, as evidenced by Amazon. In the past when we needed something, we’d go to the store. Today, we go to Amazon. It’s an online catalog that helps you quickly learn about the products that are sold on the web. Consumers are able to be more expert and self-sufficient which is exactly what they want. A data catalog serves the same function.
What is a data catalog? A repository of metadata on information sources across an organization to facilitate search and discovery, data governance and curation, and collaboration and analysis. It includes a broad range of information assets including data sets, tables, articles, reports, queries, visualizations, and conversations. It also includes common functionality like business glossaries, lineage, catalog pages, and search. Ultimately, it helps to answer core questions like how to find information, is the data usable, should the data be used, and how should the data be used.
Alation commercialized the machine learning (ML) data catalog by plotting the dots (connection), making connections (computing), and drawing conclusions. They did this by building social data search to inform their users.
Innovation through the years has resulted in 200+ customers in financial services, manufacturing, insurance, healthcare, and technology and dozens of use cases including analytics, governance, cloud, risk & compliance, data privacy/GDPR, and digital transformation.
The data platform provides a single source of reference, versus truth, given the number and sources of data and databases. It helps people find, understand, and trust the data and information they’re looking for.
We’re seeing the evolution of the data catalog into a data intelligence platform. Examples of data intelligence use cases are evolving. They began with the search and discovery of data with a query tool. However, finding the data didn’t mean you could use it. That’s where data literacy adds value by properly analyzing, interpreting, and drawing conclusions from data.
Traditional data governance programs often fail due to people, processes, and tools. However, with the right data platform, there’s a virtuous cycle of adoption - the more that people use the data catalog, the more valuable the data becomes.
In the past, requirements largely focused on the ease of finding information, collaboration, and connectivity. Moving forward, data platforms must:
Provide a broad range of functionality to support a broad range of use cases and users.
Leverage intelligence everywhere.
Use modern architecture.
Support flexible cloud deployment from on-prem and hybrid multi-cloud.
Be open and programmable to enable an ecosystem of customer and partner applications.
According to Aaron, Alation expands the audience of people who can use data, serving as Siri for the enterprise, especially for those without the data skills. An analysis is only as trustworthy as its weakest link in the data value chain so they start at the data source to make data clean, searchable, and discoverable.
We also had the opportunity to hear from Dr. Andreas Haehre, Head of Information Governance at Vattenfall, a Swedish-state utility serving Sweden, Germany, the Netherlands, Denmark, and the UK. They’re promoting fossil-free living within one generation by reducing CO2 emissions and becoming a data-driven company.
According to Andreas, a data-driven company has the core dimensions of data science skills, data, business models, technology (data platform), information governance, and innovation management. The supporting dimensions include culture, legal, and organization. They need to be precise in sharing consumer data to stay compliant with GDPR.
Andreas’ and Vattenfall’s vision is to democratize data in the enterprise whereby everyone shares their knowledge. They do this with data cataloging and sharing for everyone in the value chain. Being user-friendly with low training effort with a self-explanatory platform. They’ve gone from 50 to 400 users and from many silos of data to mostly shared data. A lot of duplicate data has been eliminated and users are realizing a lot of the data they were looking for outside the organization in the past is actually already in the organization.
Andreas feels like Vattenfall is in stage three of a five-stage transformation. Most companies are at stage 1 or 2:
Analytically impaired. Aware of data analytics but little to no infrastructure and a poorly defined data strategy.
Localized analytics. Adoption data analytics, building capabilities, and articulating a data strategy in silos.
Analytical aspirations. Expanding ad-hoc data capabilities beyond silos into mainstream business functions.
Analytical companies. Industrializing data analytics to aggregate and combine data from broad sources into meaningful content and new ideas.
Insight-driven organization. Transforming data analytics to streamline decision making across all business functions.