Part 2/2: Transforming Data to AI

Did you know an average Fortune 500 company brainstorms over 400 million decisions every day? Though these decisions are taken under a highly dynamic, interconnected, and complicated global business environment, the basis of many of these decisions include pre-set rules, gut feel, and heuristics. Enter the frame - the concept of optimal data utilization, artificial intelligence, and other technologies, which have capabilities to automate, amplify, and optimize these decisions.

Historically, this challenge was addressed through siloed approaches, but they have become almost obsolete in the current scenario. Enterprises are now addressing the data quality challenges by leveraging manual operations, developing data cleaning applications, and applying stricter rules in the database. Keeping the dynamic business landscape that frequently changes data rules, requiring a system, which is agile and intelligent enough to adapt to any changes. This makes machine learning, systems that can self-teach, the perfect answer to all data quality issues.

Though many enterprises struggle in scaling good and effective data governance, some of them have excelled in the job. The reason behind their success was adopting a centralized, top-down approach toward data governance while providing the C-suite executives a proper training on the same. It is vital to understand that good data governance is not just a department’s task, rather it covers the whole organization and requires centralized funding and involvement of the senior leadership team of each department.?

Harnessing Good Data Quality

As discussed in my previous blog, data quality is paramount, especially in times of continuous process optimization, automated decisions, and AI. Even before the data is fed into systems, there should be a pre-defined methodology for data collection and data structuring to ensure that data is sieved to remove any high-level anomaly. To achieve this, a strategy with predefined steps is required which entails:

1. Identifying, understanding, and documenting data sources
2. Data profiling
3. Establishing a data quality reference store
4. Implementing smart data validation

Artificial intelligence plays a two-way role in this process; it can be a part of the entire flow and can be benefitted from the same. This data pipeline can feed an AI algorithm while machine learning can be used to detect any anomaly in data on the basis of historical trends before it reaches way too far. This can help save manual intervention, leading to reduction in time and effort that would otherwise require spending on checking, authenticating, and cleaning data. Additionally, it also ensures that business-critical AI systems aren’t fed data, which will lead to inaccurate insights, thus wrong business decisions and hampering the ROI.

Benchmark for Artificial Intelligence

Irrespective of the industry vertical and area of operations, the greatest capability of artificial intelligence is self-learning. But where does that self-learning come from? The answer is data. For an AI system to work up on its optimum potential, it requires heaps of data. However, there are times when the two, especially data analytics, get into loggerheads.

Let’s understand it in this way: imagine AI and analytics on a spectrum. As we shift toward right from left of the spectrum, we become more capable of not only learning from the past, but also predicting the future patterns. The way toward AI maturity, including all the associated tools and systems like machine learning, has data as its foundation. For an enterprise to move along this spectrum path from data analytics to AI entirely depends on the availability of volumes of data and its cleanliness as well.

Lack of sufficient data or issues in data quality can create cracks in this data foundation, which can further shake up the integrity of everything that is either built on it or derived from this foundation. From deep, descriptive insights to data driven business decisions, every step would be impacted. According to IDC, by 2025 around 60% of the 175 zettabytes of data would be created and managed by organizations. With this amount, the criticality of reliable, accurate, and timely availability of data for effective decision making increases to another level. However, the stumbling block is still data quality. Let’s understand it in this way, predictive maintenance, an essential process in heavy industries, highly depends on the data fed into systems. Lack of trustworthy, good data in such scenarios can lead to catastrophic repercussions. For instance, in a gas exploration organization, if decisions have their foundation of poor quality data, the result can be pipeline failing and unplanned outage. This can not only lead to significant monetary losses but impact the overall safety of workers. The magic here lies in improving data quality by mastering data governance, eliminating siloed data, and data cleansing.?

Focus on ML Models

Artificial intelligence and big data share a synergetic relationship. According to a recent study conducted by Forbes, the amalgamation of big data and AI can help in the automation of around 80% physical work, 70% data processing tasks, and 64% of data collection work. Artificial intelligence requires gigantic amounts of data for learning and improving the decision making process and big data analytics utilizes AI for improved data analysis. When these two join hands to merge their capabilities, advanced analytical capabilities like predictive and augmented analytics can be leveraged in a better way to gain efficient and actionable insights from the data.

Powered by big data AI fuelled analytics, users can be empowered with robust technologies and intuitive tools required to extract great value insights from the data, fostering data literacy in the enterprise while leveraging the many business advantages of becoming a data-driven organization. By bringing AI and big data on one platform, enterprises can improve business efficiency and performance by:

• Predicting and capitalizing on new industry and market trends;
• Analyzing customers’ behavior pattern and automating customer segmentation;
• Hyper-personalizing and optimizing digital marketing campaigns;
• Utilizing intelligent decision support systems powered by AI, big data, and predictive analytics.

How to Do It Right?

In the modern business scenario, solutions are powered by machine learning, decision intelligence, or any other form of AI. Yet, if looked down over the entire marketing hype, enterprises can leverage this technology to their business advantage- if they understand what AI can (and can’t) do for them while understanding all the loopholes.

Data governance plays yet another important role in the entire process of big data AI analytics. Historically, it was considered as compliance with regulations dealing with the collection, storage, and data processing. But AI has introduced new sets of risks and challenges that need to be addressed. Now, it’s not just about collecting vast amounts of data, but also considering data’s characteristics. One of the best ways of addressing data governance is to utilize a tool called observability pipeline. This tool ensures the visibility of every process, while collecting data, unifying the same, and then cleaning the data to develop an ultimate consumable data set.

Another critical aspect of AI is doing it in the ethically right way. Just like every other technology, the utilization of AI needs to be answerable and responsible toward society. As an AI pioneer, Microsoft has designed and drafted various AI principles into practice via Office of Responsible AI (ORA) and the AI, Ethics, and Effects in Engineering and Research (Aether) Committee. This Committee advises the C-suite executives of Microsoft on different challenges and opportunities created by AI innovations. This Committee works on 6 key pillars including:

• Fairness
• Reliability & Safety
• Privacy & Security
• Inclusiveness
• Transparency
• Accountability

These pillars help Microsoft innovate responsibly, while empowering people and fostering a positive impact.

?From the above discussion, it is quite evident artificial intelligence and big data complement each other. The future of big data and AI is to make lives easier and seamless. As compared to humans, AI does a better job in extracting value from data while helping businesses understand their customers in a better way by highlighting patterns, all of which was impossible in the past. Big data, on the other hand, requires advanced software to analyze the same. Lack of AI-powered software makes it completely obsolete. Thus, enterprises need to understand the fact that AI and big data aren’t two separate entities, but opposite sides of the same coin. Those who realize it would make the most out of the two while earning greater ROIs and a cutting edge over others.