How to ready your organization to tap the value of AI
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How to ready your organization to tap the value of AI

Kirstie Tiernan, Principal And Co-National Leader - Data & AI, BDO Digital
Kirstie Tiernan, Principal And Co-National Leader - Data & AI, BDO Digital

Kirstie Tiernan, Principal And Co-National Leader - Data & AI, BDO Digital

Gain insights on where to start your journey towardsdata analytics maturity, where your organization falls or where you could be going wrong, and use cases that spotlight what’s possible for your future.

From a Data Foundation to AI Maturity

The 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 is 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?

Eventually, these will guide predicting future challenges and how to navigate them. Taking it one step further, with cognitive analytics, a machine can learn from experience and generate its own hypotheses, drawing connections from a wide range of data by reaching beyond the constraints of human thinking.

2 Into the right hands

Once a company puts the right data infrastructure and technologies in place, the question remains: Will the workforce embrace or resist it?

It is critical that the data evangelist who champions the data analytics program has the organizational clout and authority to inspire others and break down silos. Once they have taken up the cause, the next step is to work toward building a workforce of the future through talent development programs that upskill workers to leverage data in every corner of the business. Employees must trust in the validity of data-derived insights. The best way to instill this trust is by transparently educating employees on the inputs that inform data-backed decisions.

3 With the right business case

Reaching data analytics maturity requires investment in people, process, and technology.

Clearly stating potential ROI from the onset of the project is critical to help leadership and employees across the organization recognize the value in proceeding. Starting small goes a long way. Begin with proofs of concept, generating internal success stories that fuel enthusiasm, adoption, and continued investment.

What is Possible with Data Analytics Maturity

When correctly leveraged, a mature data analytics program will save businesses time, reduce risk, and boost financial return. Consider the following advantages of mature data analytics initiatives in action:

Create New Revenue Streams

A parking lot operator was able to unlock a new revenue stream by employing a computer vision model to track open parking spaces at a mall. The cameras identified the make and model of arriving vehicles—feeding that data to the mall for highly targeted advertising powered by machine learning. Based on this information, arriving shoppers received promotions within their vehicle or on their mobile phones.

Detect Fraud

A healthcare company used deep learning to review their accounts payable records to ensure there were no errors. Through this audit, they spotted one vendor and one year that stood out as abnormal. The invoice line and invoice header did not add up, all to the tune of $13M dollars. Through deep learning the AI was able to find the unexpected without humans having to tell the machine exactly what to look for. In contrast, it can be challenging for humans to sniff out fraud and identify outliers.

Spot and Resolve Gaps

A healthcare system was able to reduce patient no-show by leveraging AI and robotic process automation to send personalized text messages to patients with high predictions of not showing up. With the assistance of automation, it allowed for the rescheduling of the first appointment and finding a new patient to fill the original appointment time within 4 hours. The solution resulted in improved patient care and increased billable revenue for the hospital.

Predictive Maintenance

With AI, a railroad company was able to predict which wagons were going to need maintenance checks prior to the scheduled appointment, automatically scheduling an earlier appointment. The maintenance team was able to expedite service and reduce downtime in wagon operations.

Reduce Invoice Late Payments

A financial institution was able to predict which invoices would be paid late by using AI and robotic process automation. The institution sent personalized text messages to those with the highest likelihood of paying their invoice late and were, therefore, able to reduce late payment and increase monthly cashflow.

Why Now

Regardless of where your organization sits today, future-proofing your business depends on laying the groundwork for data analytics maturity. Your competition may not currently have the sophistication to anticipate the hazards that could derail profits or drive new revenue streams using customer data. But in the proverbial data arms race, there will be limited survivors. By starting now, you can ensure that your most valuable asset—your data— is used to edge out the competition.

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