December 19, 2017 // By David Frankenfield
Getting Ready for AI
The ability to apply Artificial Intelligence (AI) as a competitive differentiator is a huge step forward for many corporate business intelligence and analytics programs, pushing companies to adopt new practices regarding data governance. To prepare for AI, companies need data scientists and data engineers in their organization to understand, consolidate, clean, and refine their data. When it comes to effective AI, managing your data is the most important step.
The Business Value of AI
A better understanding of valuable markets, more accurate customer targeting, and insights that engage a consumer to buy are just a few examples of the benefits of leveraging AI processes within your organization. As noted in Gartner’s recent 100 Data and Analytics Predictions Through 2021, “Artificial intelligence (AI) is emerging as a core business and analytic competency. Beyond yesteryear's hard-coded algorithms and manual data science activities, machine learning (ML) promises to transform business processes, reconfigure workforces, optimize infrastructure behavior and blend industries through rapidly improved decision making and process optimization.” The insights provided through applied AI can automate your business decision-making and provide cutting edge information that can improve business decisions.
Clean Data is the Foundation of Good Business
Whether or not you are preparing your business for AI, bad data is costing your company money. A recent Harvard Business Review study found that in a survey of 75 fortune 500 companies, 3% of those companies had ‘acceptable’ data quality. The cost of bad data is often approximated with the ”rule of ten”, which states that if any part of a dataset is flawed, it requires at least 10 times the typical amount of investment for the same unit of work.
Scott Diehl, Magenic’s Practice Lead for Data & Analytics, puts it this way – “A big source of AI project execution problems is poor data quality; you can't do good statistical analysis on bad or poorly structured data - and there's A LOT of bad data out there. To meet business expectations, an organization needs to focus its energy first on providing data quality, data cleansing and data governance services, as a prerequisite to doing successful applied AI.”
“Cleansing and preparing data” can mean many things, including removing incomplete or null values, removing duplicates, consolidating disparate data sources, and setting up policies, procedures and systems that maintain data quality over time. It goes without saying that having data scientists and analysts who understand industry data is important to help coordinate data consolidation and monitor data quality activities.
A Consolidated Data Environment is an Effective Data Environment
The benefits of consolidating and cleaning corporate data sources goes beyond AI preparation. The ability to access unified, consistent, and trusted data sets, from financial information to customer records to device messages to Web site traffic logs, can assure your employees have the best information possible for their work, regardless of the analysis methods they employ.
Going Further with AI
Once a company’s data needs are met, AI can provide amazing capabilities that spark innovative data initiatives to take machine learning further. Augmenting the customer-facing enterprise with chat bots and other intelligent agents can efficiently automate complicated workflows such as applying for a loan or health care coverage. AI can simultaneously reduce manual effort while refining customer data for more accurate results.
These artificial intelligence programs are an effort between industry and market analysts working closely with data scientists to create effective interactions with customers. Significant collaboration between IT and those with an understanding of industry-specific probability models is the key to incorporate and develop the best solution for AI.
Let's Get You There Faster
Magenic has the talent to help you re/tool and reposition your data fast. Our qualified data engineers and data science experts can consolidate, clean, and create a plan for AI analysis at Agile speed. Whether you’re in the process of creating a centralized data lake, cleaning your data, or moving forward with advanced AI, Magenic has the talent and process to help.