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After the global pandemic in 2020-2022, as enterprise companies prepare for economic uncertainty in 2023, data undoubtedly will be the key to decision-making. Companies will take corrective actions in reducing the workforce, consolidating technology, and working towards efficient operations. To do more with less, businesses will have to embrace automation, improve collaboration between business and technology leaders and build the data culture within the company. Technology providers and consulting companies will need to build newer capabilities to enable customers to achieve their situational objectives and coach their customers on the technology areas such as Business composed data and analytics, data-centric AI, data access management, and efficient operations. In the following sections, I will share details that will be useful for business and technology decision-makers.
Automated Data Analytics platforms
In addition to automating infrastructure provisioning, Businesses will consider automation in building newer data integrations, transformation jobs, and retiring technical debts by moving away licensed databases to open-source cloud-based databases or data warehouses. This will optimize operational costs by 40%, improve efficiency by 60% and support a growing data footprint with the limited team.
Business and Technology Collaboration
Data analytics function within the enterprise companies will need to change from IT services management based (ITSM) to an innovation group with a growth mindset that builds data products for internal stakeholders by partnering with business teams. Business and technology leaders need to collaborate for shorter workshops to answer critical questions like defining the consumer, describing the key problem statement potential solutions, defining the minimum viable product (MVP), and working on growing these data products incrementally.
Building the data culture within the company
Data-driven culture starts from the top, where CXOs, VPs, and Director level people in the company set expectations that “decisions must be anchored with the data”. They lead by example. For example, Executives in many large enterprise companies have recently started spending 30 minutes reviewing the data and reports before participating in decision meetings. Most messaging will be led by data in their “all hands” staff meetings. They define precise metrics to measure the performance of each unit and actions within the organization so that employees understand the true situation of the business.
To implement a data culture, companies must extend data access to all the people within the organization. Technology, security, and enterprise governance teams must implement appropriate data management practices.
To implement a data culture, companies must extend data access to all the people within the organization. Technology, security, and enterprise governance teams must implement appropriate data management practices.
Explainable, Ethical, and Energy efficient AI
As per a McKinsey survey of AI respondents, nearly 2/3 of companies worldwide will have at least one use case by 2022. Companies will have to win users' trust by using customer data ethically, building the ability to explain AI algorithms; their value, and ensuring energy efficiency in the algorithms, technology, and platform decisions that the companies make. For example, Self-driving autonomous cars will achieve 40% to 50% more fuel efficient than manual driving by 2050.
Enterprise data management and governance
Businesses will need to share data with their engineers, analysts, data scientists, knowledge workers, business users, and executive users; it is paramount to have role-based access control systems (RBAC) or attribute-based access control (ABAC), which will include the ability to control data access at the data set, table, row, column, and cell level to ensure that PII data, sensitive data, secret data are protected from any unauthorized access. Therefore companies will invest in enterprise data management and governance systems.
Site Reliability Engineering-based DataOps
With the modern analytics stack, efficient data operations will be an absolute priority. Companies will embrace Site reliability engineering-based systems to ensure efficiency in operations. These include significant improvements in measuring:
- Data quality - Data freshness, data correctness, and data lineage will ensure that the correct data exists in the system, as the correct data is necessary to achieve the right insights.
- Distributed tracing - Building an application service map that points to a failure in the exact application to improve mean time to detect (MTTD) and mean time to recover (MTTD), which will improve uptime and performance SLA of the data platform
- Embracing failure in the design, reducing toil by consistent improvement, defining and measuring service level indicators (SLI), service level objectives (SLO), translating into service level agreements (SLA)
Businesses must upskill employees for new technology and find skilled engineers, collaborators, and consultants to transform their data analytics, management, and operations.
