“Data is not just the new oil; it’s the new electricity.” – Clive Humby
As we usher in an era where the potential of Artificial Intelligence (AI) is finally being realized, one question looms large.
“How can enterprises effectively harness the power of AI and Big Data while grappling with legacy IT systems and outdated processes?”
Lets answer this in this blog.
AI and Data: A Powerful Duo
AI, with its ability to process and analyse massive datasets at unprecedented speeds, is a game-changer in the world of data and analytics. It enables organizations to extract valuable insights, automate tasks, and make predictions that were once inconceivable. As Andrew Ng, a prominent AI researcher, says, “AI is the new electricity.” It’s a fundamental force that can power various applications across industries.
Recent studies highlight the significance of effectively harnessing data. A Gartner study revealed that 58% of respondents see Data and Analytics alignment with Business strategy as a top driver of success. The enthusiasm is evident as new use cases emerge where businesses want to leverage AI-driven automation. Yet, the Deloitte’s first-quarter 2023 “CFO Signals” survey paints a different picture: 64% of CFOs mention inadequate technologies or systems, and 62% highlight immature capabilities as impediments to transforming data into actionable insights.
At the epicenter of this challenge lies the vast data reservoirs that organizations accumulate. As businesses scale, they grapple with managing data across myriad pipelines and storage solutions such as ERP, data lakes and warehouses. Especially for core functions like Procurement, Finance, and Order Management, this leads to
- Delays and subpar decision-making due to information gaps.
- Dependence on manual processes and tribal knowledge.
- Challenges with complex legacy systems and protracted IT projects for evolving needs.
- An over-reliance on traditional IT applications to address modern data challenges.
Upon closer observation, these issues primarily stem from:
- Legacy IT infrastructure and outdated processes that falter when scaling insights.
- A mismatch of solutions versus the varied enterprise-wide data and definitions.
- A dearth of comprehensive suites that integrate data, insights, and applications tailored for the current business landscape.
It’s both shocking and alarming to see operations teams still rely on antiquated tools like spreadsheets and plain-text files. While these methods are functional, they are fraught with flaws ranging from inefficiency to a higher margin of error. As a result, many firms are unprepared to capitalize on the transformative potential of AI/ML technologies. Furthermore, while the market is swamped with products and solutions promising unified data platforms, most lack a comprehensive understanding of domain, technology, data, and business processes, making integration into operations cumbersome.
Companies increasingly see the value in mining their data for deeper insights. According to a New Vantage survey, 97.6% of major worldwide organizations are focusing investments into big data and AI. Given this optimistic paradigm of thought leadership, navigating the data and analytics challenges is a tough nut to crack and requires collaborating with trusted vendors like Mid Office Data.
What we think about this?
To navigate this intricate maze, we advocate leading with a “Domain and Data” approach, ensuring alignment of data and analytics strategy with overarching business objectives. This not only aids in effective decision-making but also elevates customer value.
Subsequently, the priority should be to address the technological gaps. The insights from the “CFO Signals” survey reinforce this, underscoring the trifecta of challenges: inadequate technologies, immature capabilities, and a talent crunch.
While implementing technologies for the digital finance competencies, organizations must prioritize:
- Data Quality
“Bad data is the enemy of good business.” – Peter G. Neumann
This entails embedding robust data quality processes throughout the finance data value chain. Enterprises should introspect on the efficiency of their data collection and storage mechanisms. After all, top-tier predictive tools, machine learning, and AI capabilities impose immaculate, continual data feeds. Sometimes a common data platform enables the monitoring of the data quality processes across the organization to improve overall data quality process.
- Data platform built on a Harmonized Data Model (HDM)
“The algorithms are not biased. The data is biased.” – Cathy O’Neil
A structured, centralized data model is paramount. The Data platform with a HDM offers a repository with standardized enterprise data definitions. This ensures consistent, accurate reporting and analytics, bridging gaps between various functions.
- Business-centric Analytics
“The most valuable commodity I know of is information.” – Gordon Gekko
Riding on the backbone of the Data platform, the analytics tools decipher business narratives for decision-makers. Advanced models integrated with the Data platform merge financial, operational, and external data, shedding light on trends and facilitating data-driven decisions.
In conclusion, while the challenges in the realm of Data and Analytics are multifaceted, with the right focus on domain expertise, data quality, and technological integration, businesses can unlock the true potential of AI, ensuring a future that’s not only data-driven but also insight-inspired.
Introducing the astRai Data Suite
Our astRai data suite, a cloud-based SaaS/PaaS data platform, is designed to alleviate these pain points. We offer seamless and real-time integration with various enterprise applications, harmonize the data, to prepare them for implementing actionable KPIs, building applications and sharing data through clean rooms. We have holistically thought about the solutions seek by enterprises and have come up with a unified platform to make your data journey less complicated and more effective. You can now be the driver of your industry’s competitive advantage!