Over two quintillion bytes, once an incomprehensible amount, of data is being generated by the world each day. The art of understanding the data and filtering the noise from the essence and utilizing it to deliver value to businesses is on high demand. Modern digitization is fueling a radical shift in the playbook of every company, keeping data at the heart of this movement.
With 65% of high performing companies leveraging data to enhance sectors such as customer acquisition, internal efficiency, product roadmaps, or pricing strategies, it is clear that utilizing data is an essential move for any company. But with the blinding amount of data available to companies, and the multitude of new technologies, it becomes difficult to discern where to start in the vast data and analytics landscape.
This journey is more complicated for enterprises that evolved over the time. Either through mergers, acquisitions, or by adding new processes/services, these enterprises end up with issues such as disparate systems, poor data quality, and inaccurate reporting platforms. The solution for these challenges calls for a single source of truth. Establishing a single source of truth is the most under-rated initiative in digital technologies, while it should actually be a top priority. Depending on the complexity of the data and the objectives to be met, the single source of truth can come through in the form of a data warehouse, a data lake, or even a simple database! Let’s look at how moving to a single source of truth can change a company’s collaboration potential.
We communicate with business and IT teams, go through their report creation, and understand how teams collaborate. After learning their process, we start outlining the relations between the data across different data silos. We begin our solution by consolidating the data from these segregated systems and addressing the data quality issues. We merge these data sources to create a single source of truth. This data warehouse will now be the one stop for storing and retrieving data, which allows for seamless report creation with no disparity within the reports themselves. The qualitative benefit here is a reliable data platform that enables trust, which allows quantitative benefits as instant numerical answers are produced when desired. This will enable the leaders to understand the business with more clarity, and allows them to execute their business strategy more efficiently. Building and deploying a single source of truth is just the beginning.
Having clear objectives is an essential function for any organization. Measuring success on these goals is an important but difficult task, since there are many metrics that need monitoring.
For one of our customers, we started our engagement with enabling a single source of truth and delivering a set of dashboards. Over time, however, the business started requesting larger volumes of reports, and as IT had their own priorities, backlog was piling up. While this side effect from growth isn’t a bad problem to have, longer delivery from internal IT started to have a negative impact. To solve the problem, we identified a set of SMEs and enabled a self-service BI capability for them. This took some weight off IT and allowed the SMEs to directly interact and manipulate the dashboards. More importantly, there was an acceptable governance in place so they do not run into challenges with support and maintenance of the platform. It turned out to be a win-win situation for the company as we empowered their employees to gain insights on their own, with IT focusing on improving underlying infrastructures and enriching datasets.
Up until now, we have only covered how data can be used to fix existing issues within a company, but have not touched on the greatest potentials of data analytics. Modern analytics architecture has the transformational power to identify new opportunities, understand changing tends, prevent future issues, and correlate events – even before they occur. Advanced analytics does not just help a company remain stable, it pushes boundaries and allows companies to process complex data and stay ahead of brutal competition. Optimization algorithms, decision engines, artificial intelligence, machine learning, and predictive analytics can change a company’s ability to approach new markets and promote organic growth.
Analyzing data allows us to go beyond just optimizing. It allows us to predict behavioral patterns of complex systems, sales demands, volume forecasting for call centers or raw materials, and even machine failures. All of this helps us be future ready.
We dealt with a problem for a food process and packaging company that required the use of predictive analytics. They provide machinery for the complete packaging journey of a product, including filling cartons, wrapping, and printing labels. Due to the strict nature of packaging requirements, the tolerances for mistakes on the assemblies are very thin, with even the slightest printing error causing the supply chain to halt. To combat this complex problem we analyzed data like temperature, pressure, tension, vibration, etc. from over 100 sensors on the assembly line. We performed feature engineering on this data picture to identify which sensors were relevant to print failures. From there we created a predictive analytics model which utilized incoming sensor data to alert supervisors on the floor before a failure occurred. This enabled them to enforce corrective measures before an error occurred, which saved them downtime and potential losses in penalties.
Beyond just raw numerical and character data, the massive data available to companies includes images. Photos from our phones to sensors to screenshots are present in all our data centers, and have been widely used in businesses for decades. Previously, however, images were considered too complex to glean any powerful information from; at best, we could describe an image through metadata or tagging. However, now with computer vision we can enter a whole new plane of data analytics.
In the context of businesses, we are not really looking for general purpose “seeing” from a computer. We want powerful and specific information from images that can help us categorize data and give insights. Optical character recognition, object detection, and facial recognition are practical examples of how CV can be used by companies.
Enterprises are leveraging CV to detect damages to vehicles, heavy machinery, large storage systems, hydro dams, and many more. In one of our large-scale implementations, the computer vision model has surpassed the human agent’s accuracy by 10 percentage points. What is exciting is these computer vision systems can process information and provide insights with greater accuracy than humans and other traditional mechanisms. This allows businesses to utilize human power for higher level of work.
Another incredible aspect of cognition is Natural language processing or NLP. NLP is extremely beneficial if used correctly, but challenging to utilize well, primarily because most forms of unstructured data we analyzed comes from some form of raw text. We have felt a sense of urgency from some of our partners to develop NLP applications because the amount of text analysis necessary is far greater than the number of available human analysts. In fact, we have seen a lot of new research come out in this space over the last year or so, and we have worked on a number of projects focusing on extracting entities from unstructured text, text generation, or conversational bots.
We have created numerous implementations of ML powered engines that extract information from unstructured knowledge bases such as policy documents. These bots would utilize this engine for any questions that are directed towards it, pulling the relevant information from the policy documents necessary. We implemented a multistep solution that started with digesting PDFs of different formats and structures. We then used a combination of algorithms to convert the PDF information into data structures that would be traversable by the bots query algorithm. We then used a state of the art deep-learning model to traverse the PDF document data when given a question. All of this was housed inside a chatbot!
We have covered a lot of ground here. From starting with a single source of truth to utilizing NLP and predictive models through advanced analytics, the possibilities for a company are endless. Your business is always powered by digital technology, and separating the real from the hype is always difficult. Utilizing your companies’ resources wisely by promoting the correct technology can elevate your practice and allow you to stay ahead of your competitors!