Al Mahdi Marhou
, May 23, 2024
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The quality and integrity of your data forms the foundations of AI success.

As Artificial Intelligence sweeps across the landscape, its applications offer numerous possibilities and advantages to companies looking to leverage their data to the max. The evolution of AI, machine learning, language learning, and other technologies has paved new roads to processing and analyzing data, forming an elaborate ecosystem of endless possibilities for increasing competitiveness.

AI is the new kid on the block, but data and the importance of data analytics is as old as business itself. From handwritten notations to Excel spreadsheets and the emergence of databases, data has long been the driving force behind informed business decisions. Advancements in technology gave rise to things like data warehouses, where data can be collected and stored, and cloud solutions have now enabled much larger warehouses to be built in virtual environments. Simultaneously, the field of data analytics has expanded exponentially as database management systems have opened the doors to those storage facilities.

While AI is the buzzword behind a tidal wave of intrigue and curiosity spanning the globe, artificial intelligence is the pinnacle of a vast ecosystem upon which data forms the foundations. The rapid spread of AI has excited the masses, and everyone wants a piece of the pie. However, like a successful construction project, you must first lay the foundations upon which a beautiful house can be built.

Establishing robust data foundations for AI success

The sheer growth of data and its importance for businesses has spawned new analytics solutions designed to simplify the extraction of key performance indicators (KPI) and trends. AI has emerged as the quickest way to automate the process of finding meaning in endless streams of data. However, the more data there is, the harder it is to uncover the critical information that companies need to bolster their positions. AI offers numerous paths to achieving that goal, but data quality is the X factor that enables it to do so.

Effective data analysis follows five important steps, from identifying the core needs of a business, to collecting and storing its data, cleaning and preparing that same data, analyzing it, and then visualizing and communicating the findings in actionable formats. Most companies have long understood the importance of gathering and retaining data from all aspects of their operations, but many remain oblivious to the fact that the most sophisticated AI-driven data analytics tools are only as good as the quality of the data they are tasked with analyzing.

Prioritizing data quality over quantity

AI is moving the ball forward by harnessing the power of several tools in the digital transformation toolbox, including machine learning, computer vision, language learning, and more. But regardless of the technology you implement to analyze your information, data quality is the key to successful outcomes.

Failure to understand the importance of data quality can lead you down a meandering path to poor decision making as endless gigabytes of stored data may ultimately offer very little insight into your operations. For example, variations in the reporting of the same information by multiple departments can diminish the integrity and quality of your data, ultimately leading to the analysis of inaccurate metrics.

Avoiding the ‘garbage in, garbage out’ pitfall

Sometimes errors can be minor and obvious, but they can destroy the quality and integrity of your data if not addressed. That is what we refer to as ‘error propagation’, where even a small error in your input can propagate into a much larger one upon output. In 1999, NASA’s Mars Climate Orbiter basically disintegrated just 10 months into its journey. The cause was ultimately a case of ‘garbage in, garbage out’, where data provided by the navigation team in Europe, reported in metric units, was inputted by the US-based team in imperial units. That error in data quality resulted in a $125 million dollar loss.

The moral of the ‘garbage in, garbage out’ story is that you must ensure the key dimensions of data quality in your input - accuracy, completeness, timeliness, and consistency - before you can successfully leverage the insights obtained from your output. Remember, AI-driven analytics tools don’t have any knowledge beyond what you introduce to them, so the integrity of the output they provide is 100 percent dependent on the quality of your data.

Business Intelligence: your pathway to data maturity

Business Intelligence is the path to transforming basic insights into data quality ready for true AI innovation. Business intelligence solutions draw upon numerous technologies to transform raw data into actionable insights, including data integration, data modeling, data mining, data analysis tools, and dashboard tools for reporting key metrics in a variety of formats. By combining the power of analytics, data management, reporting tools, and much more, they provide essential descriptive analytics for harnessing AI’s predictive and prescriptive analytic capabilities. In other words, to maximize the power and efficiency of AI data analytics, BI tools must first be deployed to identify and organize your quality data.

As a lead data scientist with Alithya, I am part of a global support team that endeavors to provide clients with powerful data analytics tools to process, interpret, and extract their information to make the most informed business decisions possible. Our skilled professionals hail from numerous industries and are specialized in a variety of technologies and processes, and together we apply our collective intelligence and collaborative nature to helping clients understand the pathway to data maturity. That process typically begins with learning sessions, followed by an evaluation of the general quality of a client’s data.

Using cartography, we can build a visible profile for the client using metrics to reveal gaps where the quality and integrity of their data may be lost. We can also build a BI dashboard to create a visual presentation, enabling clients to track the movement of their data and to see the emergence of trends in real time. Collectively, the dashboard consolidates multiple BI functions in the data ecosystem in a single location, like nets skimming the surface in search of nuggets of quality data. A BI dashboard can also be tailored to maintain the integrity of the data throughout its lifecycle, while organizing it all in a single location in a language that the client can understand.

Decision-making: streamlining BI and AI integration

Once a robust BI platform has been implemented, and quality data has been segregated, powerful AI tools can be seamlessly introduced into the ecosystem to generate actionable intelligence. Reports, charts, tables, graphs, and more can be tailored to provide clear overviews of essential metrics for important decision-making, with the peace of mind of knowing that your AI analysis is based on accurate, quality data. The BI dashboard thus becomes a source of knowledge and information, as well as a layer against risk by providing oversight of the entire data ecosystem.

Securing your results

With organizations constantly facing concerns over data privacy and security, data analytics and cyber security go hand in hand as part of a broader strategy. As the importance of data increases, so too does the need to protect that data in order to retain its value. The edge gained from tremendous insights derived from data analytics can be lost if that same data is available to everyone.

Data is a source of power, and therefore cyber security and strong data governance   are critical components of an organization’s data ecosystem. That process includes a thorough understanding of legislation designed to protect not only your own data, but also that of your customers. In recent years, protective layers have been legislated to ensure that data is owned and retained by the company it belongs to, but having a robust cyber security bubble around your data remains essential to the process.

Preparing for the future

The importance of a company’s data fosters the need for relations with trusted advisors. Accordingly, Alithya has earned a reputation for building collaborative partnerships with its clients, deploying a business model designed to ensure favorable outcomes for all parties. That process typically begins with our Roadmap to AI, a perfect starting point for addressing data as the first brick in the data ecosystem foundation.

Our Roadmap to AI is a consulting component designed to help chart a course to the best use of a client’s data. Before discussions of AI even enter the conversation, the process focuses on the best path forward to gaining insights from quality data. In some cases, the functions of a robust BI dashboard may be just what the doctor ordered. Above all, the objective is to show clients what they can do with a variety tools, at a variety of different investment levels, and sometimes the optimal solution is less costly than anticipated. Contact us to chart your roadmap to AI today.

Regardless of where the roadmap leads you, data quality is the first step to ensuring efficient analytics. A robust data analytics future, adapted precisely to the needs and future needs of your business, begins from the bottom up. Once you have a clear global overview of your quality data, decisions for choosing the best tools to analyze and extract it emerge with greater clarity. That clarity helps ensure greater ROI, as optimal data analytics processes can enable companies to do things that they have never done before, and to respond in real time to actionable intelligence on trends, market movements, customer needs and demands, and so much more. 

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