Cleaning Up the Crime Scene Unnecessary; Bring Your Data As Is
by Russ Loignon, SVP, Market Strategy
The adage, “If you count your pennies…the millions will care of themselves” is clearly a reference to focusing on the small things one at a time; the big things will take care of themselves. In this article, we dive deeper into the ways in which a simpler yet controlled manner in managing your data and extracting value can yield tremendous benefit.
To level set business value to be gained from data, it is important to understand how technologies such as AI can be that facilitator. Gartner analyst Douglas B. Laney authored Infonomics, in which he refers to three tenants he identifies a data value: Monetize, Manage and Measure. Laney speaks of how best the entire CxO suite can harness the value in the data their firm possesses, customer data or other available data. By leveraging new tools to better include and assess this entire data view, firms are realizing greater insight and overall business value.
We at Lucd have seen the same results when firms begin to take these first steps and turn their firm into beneficiaries of digital transformation through AI. Even those clients who have yet to take those first steps, when discussing the possibilities in a broader discussion, tend to “get it”. It is usually at this point of revelation that the discussion turns into action. Either way, you must get started and take control. Studies show that those firms who are slow to incorporate an AI strategy are potentially doomed to be left behind, be it individual leaders or the business itself. In taking control, Enterprise AI provides the best means for success.
Too often we see clients who feel they have to spend time and money to fully “clean” their data or get the “right” data before moving forward with an AI strategy. These approaches do not facilitate the best results. There is tremendous value in the actual (dirty) data. A clean data approach is often likened to that of a crime scene that has been cleaned up prior to investigation. When that happens, the true evidence is either compromised or the real data is lost. The same is true with AI. Bring your data as-is to start. Even a small set of data can bring results. Surely, more data is better for accuracy; however, if you start with “pennies” of data, much can be learned and benefits gained.
A simple spreadsheet or PDF are examples in which AI can help turn basic data into millions of dollars in benefit across the organization. Enterprises are ripe with often overlooked yet valuable data that can be converted into beneficial business insight. Simple and easy data that exists in documents or spreadsheets can be used to assess workflow or procedural patterns. Resulting insight may accelerate business process and or sales processes that can deliver increased revenue and profits. Legal departments who are overwhelmed with contract reviews can have them run through AI models that identify common “redline” issues and thus can expedite the review process as the AI learns contract review.
Emails can be reviewed by AI solutions to better understand individual or organizational insights from key words to identify patterns or trends. This can be incorporated into sales or customer service practices to improve Net Promoter Score or revenue.
Some retailers, who, currently don’t have resources – be it financial or personnel to push forward with an AI strategy – are still taking that initial step. By intuitively using customer and internal information to improve, customer satisfaction is being ingested into AI models. These results are sometimes not initially obvious; however, when run repeatedly and the system better learns, results are gained. The barrier to leverage AI solutions is getting lower. The AI vendor community is growing, and even mid-size and small businesses can gain benefits from their data.
Not every organization needs to hire a Chief Data Officer (CDO) but it does need someone(s) driving the initiative and strategy to drive value from data. Gartner predicts that “by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy, acknowledging their extreme deficiency. Data and analytics leaders should evaluate and close competency gaps today to secure the data-driven enterprise of tomorrow.” READ MORE>>