8CIOReview | | FEBRUARY 2024IN MY OPINIONGain insights on where to start your journey towards data analytics maturity, where your organization falls or where you could be going wrong, and use cases that spotlight what's possible for your future. By Kirstie Tiernan, Director, Forensic Technology Services, BDOFrom a Data Foundation to AI MaturityThe path to Artificial Intelligence (AI) maturity must start with a sturdy data foundation. The ability of a business to move along the spectrum from data analytics to AI depends on the availability and cleanliness of that data.Cracks in the data foundation will weaken the structural integrity of everything built upon it--from descriptive insights to data-backed decision-making. Essentially, insights are only as good as the underlying data on which they are based. A significant portion of enterprise data is either trivial, irrelevant or cannot be read by the systems in place. Extracting insight from data is often constrained by inconsistent naming conventions, duplicate data, and incomplete records.Organizations would be wise to start good data management habits now, being mindful of the value they hope to derive from analytics in the future. Eventually, every business process--from core operational processes like customer acquisition to management processes like risk management and support processes like accounting-- should be data-driven, with analytics embedded throughout. The journey to operationalizing analytics across the enterprise may start small, with ad-hoc adoption of analytics in dashboards and reporting, paving the way for more sophisticated analytics tools and business intelligence.Where Organizations Go Wrong Successfully reaching data analytics maturity can best be attributed to getting the right data into the right hands with the right business case. Most failures occur in low to mid-maturity levels before analytics have fully permeated every aspect of the business--and while doubts and discomfort can weaken employee adoption.These factors can make or break a successful data analytics program.1 Getting the right data Garbage in, garbage out: Data must be refined, cleaned, and governed to set the right foundation. A centralized data strategy and scalable architecture to support the future state of the program are essential. Only with the right data foundation can businesses derive visibility into:· Descriptive Analytics - What happened?· Diagnostic Analytics - Why did it happen?· Predictive Analytics - What might happen?· Prescriptive Analytics - What should I do?· Cognitive Analytics - What don't I know?Kirstie TiernanHOW TO READY YOUR ORGANIZATION TO TAP THE VALUE OF AI
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