As businesses compete to stay valuable for their consumers, cloud support services further fuel the trend of adapting to the times. Digitizing business operations is now as essential as training your teams to achieve set revenue targets – without both, your business is bound to lag behind and lose customers. As a matter of fact, the industry of software development in Sri Lanka has been in a constant state of evolution, thanks to new trends that are continuously being rolled out. Clients are quick to demand the infusion of such newfound advancements into their custom-developed applications, in a bid to offer greater convenience for consumers, and stay one step ahead of the competition.
While the latest digital technologies abound for making the most lucrative digital systems a reality, it isn’t going to be possible without the right caliber of data. Again, timing matters here, as raw and unstructured data repositories further require filtering in order to identify patterns lying within. Of course this is an initiative that is taken with the right tools and people in your business teams. Contrary to popular perception, data alone isn’t sufficient to reveal valuable insights, to aid decision making processes for business leaders.
From having enough data, to having the right skill sets in order to crunch and churn, the task of data analysis is as dynamic as it is demanding. However, as more and more business leaders realize the massive potential which lies in the realm of big data and the analytics world at large, B2B technology companies are rising up to the challenge of making big data accessible, secure and affordable for long-term retention. With numerous analytics services now even available on a SaaS basis, the facilitation of big data is something that even smaller players can afford; it’s not something that is reserved only for larger corporations anymore.
In spite of the complexities, big data is increasing in demand – with companies keen on doing what is crucial to accommodate it. In this article, we focus on where big data is being leveraged the most, as well as insights from different industries, pertaining to real-life use cases. Read on to know more!
While big data has long since been associated with reporting, data analytics and business intelligence, it is now garnering attention in another highly powerful environment – AI. Vastly dependent on large streams of filtered data, algorithms powered by AI and machine learning train to improve outcomes, with each additional layer of information. As a result, big data is now a prerequisite for most AI-based algorithms, as programmers’ endeavor to improve the quality of outcomes with every single iteration.
At the same time, the goal is to also make AI-powered algorithms independent, as it is exposed to new data. By creating a steady cadence of data that can be collected through regular daily activities (such as website/app engagement and transactions), algorithms can be ‘taught’ to learn better – and subsequently deliver predictions that are more accurate with each passing day.
A good use case for AI and big data is sentiment analysis. By applying Natural Language Processing (NLP), algorithms can identify trends between different words, phrases and other linguistic elements. In turn, these patterns can then be tallied with varying human emotions and moods to reveal what the overarching sentiment of a certain block of text entails.
Sentiment analysis is a big boon for analyzing customer reviews in bulk, and determining whether feedback is positive, negative or neutral. Businesses can then address anything that is problematic, in order to improve products and customer satisfaction, for better business outcomes.
Telematics and fleet management have long since gone hand-in-hand, especially after the ability to install OBD (On-Board Diagnostics) trackers, smart dash cams and a variety of other equipment, in moving vehicles and ancillary assets alike. Whether it’s a commercial truck or forklifts in the warehouse, all assets can now be facilitated with telematics, which not only capture a wide variety of data – but can also respond to changes on a real-time basis.
From monitoring temperature in refrigeration trucks, to conducting weight control, telematics has been a gamechanger for the fleet management, supply chain and logistics industries. On top of that, OBD trackers and dash cams are also particularly adept at monitoring driving violations. Whether it’s detecting driver fatigue or rash driving, previous logs can be used to improve safety algorithms in order to make driving safer for those behind the wheel – as well as reduce liabilities on the part of businesses.
As assembly lines embrace automation in the form of robotics, the manufacturing industry has been on the receiving end of maximized productivity, while slashing operating and labor costs. Through automation, various business units pertaining to the average factory (such as inventory, quality inspections and yes, assembly lines) have also reduced the margin for error, when it comes to the final product.
Automation can be integrated in existing factories, to reduce costs and increase efficiency. This has long since been normal even with smaller businesses that are still novices in their field of expertise. However, this can all be taken a notch or two above with big data. Train algorithms to observe new streams of data from daily, regular activities, in order to further improve at scale. This level of versatility can also render a manufacturing operation that is mostly (if not completely) autonomous, as responses to changes can be facilitated sans any manual intervention.
As tools and equipment are automated for activities both regular and urgent, big data is also being utilized in another key area, for better productivity and reduced costs – the workforce. Whether it’s full-time personnel or independent contractors, workforce optimization software is built on the premise of streamlining cadences across hierarchies, departments and even internally amongst individual team members.
However, performance management is another crucial aspect that is delivered by workforce optimization; from evaluating the quality of work completed to identifying which associates are most helpful towards customers, workforce optimization can make all the difference between an average workforce, and an absolutely great one. Big data can make final insights much more intuitive by making accurate predictions, based on previously gathered data.
From forecasting training requirements, to whether an employee is bound to improve after their probationary period, big data is a useful component when it comes to workforce optimization for businesses of all volumes and industries.
With cyber breaches constantly around us, our digital footprint is constantly open to vulnerability. From password infringement to a loss of financial data, cyber breaches have always proven to be catastrophic – both in terms of revenue as well as reputation. Add to this a shortage of cybersecurity professionals, the world over. This combination has further ramped up the stakes for businesses and cybersecurity vendors alike, as both scrambles to maintain protection at all times.
Within big data lies the answers to your business’s most pressing quandaries – as well as certain secrets that may turn out to be absolutely priceless, for pursuing success. The world of cybersecurity is also no exception to this rule. Herein lies trends, patterns and everything else in-between, when it comes to the habits of both legitimate users, and those with malicious intent. However, big data alone isn’t the challenge, when it comes to cybersecurity – organizing such vast repositories of data is what is.
This is the reason why parsing relevant data points from the rest is more important than ever in cybersecurity and is carried out with a combination of components at hand. While numerous cybersecurity vendors automate threat detection with bots powered by AI and machine learning, security analysts are always on the lookout in Security Operations Centers (SOCs) that operate on a round-the-clock basis.
Services delivered by the healthcare industry have been soaring in demand with only limited resources available to attend to every patient’s requirements. At a time like this, big data is a massive help to healthcare personnel at large, as a wide range of healthcare equipment can be made autonomous in a streamlined yet highly reliable manner. As sensors capture vital medical data, these can then be crunched for predictive insights so that healthcare professionals can conduct preventive/proactive care for certain patients, as opposed to when conditions deteriorate.
On the other hand, smart devices can behave as sensors, which upon capturing vital statistics, can then crunch numbers to trigger certain actions within various forms of healthcare equipment. Although responses can be triggered on a real-time basis, these can be sped up further with edge computing technology; critical devices can swiftly process data within the confines of its device, instead of communicating back and forth with a distant server.
Customer abandonment is a phenomenon that is all too real – and that no business is immune to. With limitless options available, customers are spoilt for choice, and there is no remorse when skipping from one brand to another, even if it’s due to the slightest lag or glitch. Software outsourcing in Sri Lanka, for example, is home to the software development endeavors of clients who, as part of an Agile process, are constantly reiterating their applications to satisfy their customers better.
The alternative? A loss of customers, as they look towards competitors to find what they need. With so much at stake, businesses are motivated to use big data in order to understand what truly makes their customers tick. Data-driven decisions therefore pave the way towards better products and customer experience, in a variety of ways.
For one, big data can be used to recommend products that users may possibly be interested in, based on their engagement habits. Or, improve chatbots by enabling them to make suggestions that are more relevant, based on previous transactions that the customer has had with your business.
As many businesses join hands with their cloud service providers to form dedicated partnerships (such as being an exclusive AWS partner, for example), organizations are rendered a great level of convenience. With any enterprise-level cloud service now available from under one roof, companies need not hop from one vendor to another, to find the perfect solution. Likewise, your cloud service provider may already offer their very own, proprietary BI service. But just like any other analytics tool, it needs data to deliver insights.
BI, as a result, is no exception. Not necessarily a specialized industry per se, BI is a component that will fit within any organization to aid mindful decision making – with the support of big data, of course. On top of that, independent BI/analytics vendors are plentiful in the B2B software space, with some targeting niches, or smaller/startup companies. As BI and big data go hand-in-hand, the vendors offering SaaS-based BI applications offer numerous tools to help make better sense of existing data. While many components will require technical assistance for configuration, low-code/zero-code platforms are also slowly proliferating the digital landscape.
As businesses continue to embrace the digital landscape to engage with their customers, big data is at the forefront for supplying insights that will enable business leaders to call the right shots. With the right tools, personnel and programming power, big data can be a valuable asset to any business, as even the smallest, most niche data points are scrutinized to reveal patterns that could potentially make or break your business.
With every industry having jumped on the big data bandwagon, it has now become a vital component for any analytics strategy. From powering IoT devices to act and respond as needed, to offering a bird’s eye perspective on what lies ahead for the future, big data can be a treasure trove of data for the business that is keen to learn how it’s functioning – and what it can do to improve.