Data of an organization is considered its most vital asset. It is the source of information, knowledge and the wisdom for correct decisions and actions. In fact, relevant, complete, accurate, error-free, timely, consistent, meaningful and usable data helps in the growth of the organization. Therefore, managing the data is very essential. A sound data management process will help companies to align strategies and identify areas of growth. A comprehensive data management system helps managers and executives to access the information they need whenever they need it. Data management involves data entry, data back up, data cleansing, and more. Reliable data cleansing services help organizations to obtain accurate and relevant business records, ensure better sharing of information across departments, improve response rates with correct contact details and maintain the standard industry database file formats.
Nowadays, many organizational leaders are attempting to accelerate the deployment and adoption of artificial intelligence (AI) as it is the cornerstone of digital transformation. However, most of these organizations are still struggling to enhance adoption and interest in analytics as better decision-making cannot be fulfilled without widespread adoption, in spite of emergence of business intelligence (BI) platforms. An organization must first have a solid BI strategy rooted in the core pillars of people, process, and platform to have any chance at success with AI. Recently, many organizations have moved beyond basic descriptive analytics into more diagnostic analysis; however few have created a true self-service environment that are able to embrace the benefits and risks of AI. Without this foundation in place, efforts to speed up AI deployment could lead to negative outcomes like incorrect decisions resulting in lost revenue opportunities, penalties, or even long-term damage to the reputation of an organization. So to avoid common pitfalls, organizations seeking to bolster investments in AI and accelerate its adoption should first evaluate the current status and foundational stability of their BI program.
The stakes associated with AI are exponentially higher than those of BI. In fact, BI is largely focused on understanding what has already happened, primarily through key performance indicators (KPIs), while the benefits of AI and machine learning reside in what they can offer in higher value predictive and prescriptive analytics. Higher risk is often associated with high potential reward. If KPI is reported incorrectly through a report or dashboard, then it won’t be viewed as a disastrous event, but this may not be the case, if a critical business decision is ill-informed by a poorly trained algorithm. Data is the foundation of an AI system. Hence, the quality and reliability of AI-enabled prescriptive recommendations or automated tasks are directly connected to the quality and reliability of the data used to train the system. Organizations that have failed to invest in sound data management practices or have struggled to build traction and confidence in their BI deployment stand little chance of successfully implementing AI.
In fact, many organizations have invested in and implemented sound data management techniques. However, organizations can only advance in analytic maturity if employees analyze the data and use the resulting insights for decision making. Adoption of BI has been consistently low since its inception, with a relatively small percentage of organization’s users actually embracing BI and analytic capabilities. Even though the BI market is shifting towards a modern self-service model, extending analytical capabilities to a broader audience, most organizations are still in the infancy of overall analytic maturity. This is due to the time and complexity associated with creating a true culture of analytics. For an organization’s users to train an AI system, they must first have a focused interest in the outputs along with the aptitude and competence to properly manage the inputs. When they begin to enhance their data literacy levels by asking better questions and exploring new datasets, their requirement for more advanced analytic capabilities often follow concurrently. This promotes an environment where an AI implementation can succeed.
The extent to which AI will succeed within an organization is based on the decision makers. If organizational decision-makers favor instinct over data, there is likely to be less chance that they would be willing to trust machine-generated insights and recommendations. In the face of business process decisions, a leader who has never embraced a data-driven mindset will likely reject any “black box” AI solution and revert to instinct. Decision makers must attend to the underlying issues contributing to hesitant BI adoption to build a successful AI implementation. This begins with an honest evaluation of an organization’s data assets to determine if they are suitable to serve as an input into algorithms that power AI. A comprehensive data strategy should be developed and utilized to address any gaps or weaknesses in the areas of data governance, quality, cleansing, cataloging, security, or metadata management that surface during this assessment. During the time that the organization is building this foundation, determine if there are departments or teams that have already established a solid BI program or developed robust analytic processes that drive their decision processes. It is normal to evaluate AI to further optimize decision making. These teams can serve as a model for other areas of an organization as they make steps towards analytic maturity.
