5 min

The C-suite is expanding. It’s a term that doesn’t immediately resonate with everyone, but the C-label is of course intended to signify ‘chief’ and serve as the prefix to chief executive officer (CEO), chief operating officer (COO), chief finance officer (CFO), chief technology officer (CTO) and so on. While CTOs have long fought to have a place at the boardroom table (commercial organizations typically rank CFOs and their financial purview as more important than the firm’s technology backbone), that trend is changing. As the CTO finally gets a place at the table (both figuratively and literally), they will be championing the cause of their closest counterparts, the chief information security officer (CISO) and, now, a new breed of C-suite IT leaders whose responsibility gravitates around data – so who are these people and what tools will they use?

The rise of the data team in question here demands that we consider a new group of job title designations. We can now think about interacting with the head of data quality (HDQ), the master data management (MDM) leader, the data governance tzar (DGT) and the plain and simple chief data officer (CDO).

What are the key data jobs?

Whatever the title, these technology engineering managers need to use platform-level tools to handle data management tasks ranging from data security, data reporting (for governance), data de-duplication, data warehouse and data mart management, data lake exploration and – although it is comparatively new ground – data marketplace exchange tasks and more.

Offering technology designed to shoulder a number of these tasks is the difficult-to-spell but fun-to-say Toronto-based data engineering company Ataccama. A specialist in unified data management (Ed: is there a non-unified approach to data management?), the organization is presumably named after the vast expanses of the Chilean desert of the same name, but with an extra C for good measure.

Clarifying this use of the term unified, Jayesh Chaurasia, MDM & data governance analyst at Forrester Research says that in a world run by data, unity is not just efficient; it’s essential for what he likes to call sustained ‘business brilliance’ these days. “The need for a unified platform is clear: You can have each of these capabilities individually but unless they seamlessly integrate with each other, you spend more time managing the system than the data,” said Chaurasia.

Not to be left out in the race to check the product box confirming a degree of generative AI in its platform, this month sees Ataccama announce the release of its ONE AI service with gen-AI on board. The company insists that AI technology has been an essential part of the Ataccama product for more than five years, but the ONE AI release now builds upon that foundation to help data leaders (in our team referenced above) address two major challenges: 

  • Automating routine, manual work so data teams can invest their time into higher value tasks.
  • Making it easier for all types of users to access trusted data to increases the value that people across the business can derive from data governance initiatives.

Deeper into data tasks

Here we see that ONE AI is an AI engine that powers automation across the extensive capabilities of the Ataccama ONE platform. In addition to features available prior to the release of ONE AI, including anomaly detection, record volume matching, time series analysis, freshness monitoring and record-level outlier detection, the platform now offers automated data rule creation and assignment. This means that any user can improve their organization’s data quality by providing AI-augmented recommendations and creating actionable data quality rules via plain text conversions, without any need to code.

“AI will allow organizations in our market to innovate faster and explore completely new competitive opportunities, accelerate access to business insights, and grow business,” states Martin Zahumensky, chief product technology officer of Ataccama. “[WIth out addition of generative AI], it was extremely important for us to cut through the hype and deliver something tangible, useful, governed and safe into the hands of our users as soon as possible.”

How AI helps data management

The release of ONE AI means users can access generative AI and traditional AI technologies in one place. The company says it has been working with AI since 2016 and will continue to leverage traditional AI for use cases such as data classification or anomaly detection, where significant amounts of data need to be processed.

“There is no doubt that AI is suffusing its way into many software products, including those of data management,” said Andy Hayler, practice leader, data as an asset at Bloor Research. Agreeing with Haylar is Stewart Bond, VP of data intelligence and integration software at IDC. “The advent of generative AI in data management heralds unprecedented possibilities, yet it’s crucial to recognise that gen-AI is not a standalone solution. A synergistic approach combining gen-AI’s innovative capabilities with traditional AI’s proven strengths offers the most effective strategy, giving vendors with prior AI experience a significant edge in leveraging gen-AI’s potential.”

Further data functions

In the field of AI-powered data governance, the release of ONE AI brings data documentation to the platform. Users can access gen- AI for automated data asset categorization, classification and the creation of descriptions. Ataccama’s AI-powered business term suggestion features remain available, reducing the burden on data stewards and business users to provide manual input.

Also here we find assisted user experience. ONE AI allows users to ask for what it is they want to know, saving time by reducing the need to sift through documentation. Finally, there is SQL Generation – ONE AI claims to ‘ends the need’ to learn and write in SQL. Users can simply use plain language to ask for data they are looking for and ONE AI performs the SQL translation. SQL queries can also be interpreted and plain language descriptions are provided to users on demand. 

Chief data leaders of all descriptions will clearly need some or all (and more) of these functions if they intend to get a credible shot at joining boardroom discussions. Whether they choose to showcase the full toolbox of functions being carried out across their organization’s data fabric or not is up to them; given the presence of gen-AI now manifesting itself in the platform detailed here, it may be better to look the non-technical C-suite management team in the eye and say ‘oh yes, our data layer is managed by generative AI’… and just finish with a winning smile.

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