The AI-fication of Data Engineering Bolsters AI Development
For AI to produce high-caliber output, be it analytics insights or completely new content, AI needs well-sourced, standardized, and well-governed data. In short, one of the primary things that AI needs is data engineering. Yet as of this writing, AI has advanced so much as to be able to help fulfill data engineering tasks. A new slate of state-of-the-art AI tools can now take a significant amount of data engineering workloads from the hands of data engineers in a transformative process we call “AI-fication.”
Thanks to the power provided by AI, IT leaders can better address data management problems, such as:
Through the AI-fication of data engineering processes, data engineers are freed up to do other tasks, such as provide their insights to business decision-makers. Given the engineers’ intimate knowledge of business data, they are likely to have ideas pertaining to the business that could be tapped if only they were free to be tapped. Additionally, AI tools empower data developers and engineers who lack deep expertise to accomplish advanced tasks. For example, data transformation engine users can talk to AI assistants using natural language to ask for help on writing queries in SQL or other language that the engine requires.
Navigating the Data Jungle in ESG and Sustainability Reporting
With the rigorous reporting obligations under the Corporate Sustainability Reporting Directive (CSRD), enterprises face a myriad of challenges in sustainability reporting that is rooted in data collection and management. How they handle sustainability data ultimately affects the accuracy and credibility of their reports. In this first part of our series on data management for ESG and sustainability reporting, we explain the common pitfalls that enterprises should avoid when handling or collecting data.
The most common that we’ve observed with organizations that we’ve worked with are their dependence on files that cannot support complex analysis and modern data management infrastructures as well as manual, error-prone processes of updating and managing sustainability data. Additionally, most of the data is stored in various sources, which leads to miscommunication. This misunderstanding often results in duplicate, redundant efforts in data collection.?
Relying on flat files for sustainability reporting
In a nutshell, flat files are simple data files that contain records without structured relationships. These include .csv, .txt, and .tsv files. Unlike databases that can enforce data types, constraints, and relationships among data, flat files typically organize data in a plain text format where each line represents a single record.?
For example, electricity usage or fuel consumption data might be stored in flat files where each line provides information on daily or monthly metrics for different facilities or departments. Similarly, emissions data, including CO2 and other greenhouse gases (GHG), is often recorded in this format to manage company emissions reporting. Waste management data, such as amounts of recycled materials and waste generated, are also typically stored in flat files. This format is similarly used for recording sustainability metrics within the supply chain, like supplier sustainability scores or data concerning the sourcing of raw materials.?
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Data Governance Strategies for Amplifying Analytics ROI
Producing business insights from multivarious analytics tools is one thing, but turning those insights into actual business value is another. In fact, Gartner’s 2023 CDO survey revealed that 69% of Data & Analytics leaders are finding it hard to deliver quantifiable return on investment (ROI) from their initiatives. In this article, we will show how some blockers to value creation are data governance problems, and that strategies that are based on a grounded and business-oriented approach are effective in removing those blockers.
According to Google, data governance is “everything you do to ensure data is secure, private, accurate, available, and usable. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.” Data governance is a form of centralized data management that is principled. It complies with standards imposed by stakeholders, government agencies, and industry associations. It also follows internally set standards or data policies for how data is collected, kept, used, and disposed of. For example, a policy can set what type of personnel can access a certain type of data. Since we’re discussing ROI, it seems counterintuitive to tack on hefty data governance costs into the equation. However, if people think that compliance is expensive, wait ‘till they try non-compliance.?
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Poor data governance impedes value realization
Without proper data governance, enterprises face many challenges, such as lack of data consistency; difficulties in upholding data security, privacy, and regulatory requirements; the formation of data silos; and the lack of data observability, which creates the need for repeated checks and validations prior to using data.?
Lack of data consistency
In the creation and use of data systems and analytics tools, stakeholders, such as business units, data engineers, developers, and data users must agree on the terms of data curation. These terms include what data is gathered from where, how the data is standardized, formatted, and understood, and what level of quality is acceptable for data to be ingested. If data scientists acquire the data sets that do not match what business users require, then the tools built with these would provide results that users don’t need or deem to be untrustworthy.
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