Predictive Analytics, IoT, and AI
The persistent torrent of technological innovation has secured predictive analytics a permanent and indispensable position in all businesses today. Traditionally, businesses have had to spend hours looking at past results to make decisions about the present. Predictive Analytics, however, allows them to look at the real-time results to make predictions about the future. This increased foresight allows businesses to recalibrate their operations around the predicted trends to ensure improved efficiency on all levels.
The dramatic influx of big data, IoT (Internet of Things) devices, and artificial intelligence (AI) advancements have made the process of predictive analytics both more complicated and more fruitful. With the steady rise of social media users, Google searches and connected IoT devices, the amount of data available to businesses has increased and continues to increase exponentially. This massive increase of big data makes it more difficult for businesses to go about sourcing, storing, and maneuvering through this information in order to make meaningful predictions. At the same time, it does allow the predictions to be more accurate, and it sheds new light on previously disregarded areas of consumers’ lives.
Finally, the advancement of AI helps to make this process a little easier, as machine learning and other AI capabilities allow data mining to be much quicker and more effective than before. It is crucial that developers wrap their heads around this complicated process, and that they harness the tools available to help their businesses thrive.
Solving the Big Problem with Big Data
Gone are the days when we used to talk about ‘gigabytes’ of data. We no longer even talk about ‘terabytes’ but have moved on to the ‘petabytes’ of data due to the rise of internet usage and IoT devices. The linguistics of speaking about data is easy enough to process. Actually acquiring, storing and processing these petabytes of data, on the other hand, requires much more effort and expertise. Before businesses can get to the analysis phase of predictive analytics, they need to make sure the data is clean, secure and ready to be analyzed.
Making use of the correct tools in order to do this is crucial to make sure your data is secure and manageable. For instance, a previous article lists four essential AWS tools to improve your workflow to ensure the sheer amount of data does not hold you up. These tools include Amazon S3 Storage, AWS Code Pipeline, AWS Cloud 9 and AWS Code Build.
Understanding AI and IoT in Relation to Analytics
The amount of data coming in from so many different areas is definitely too overwhelming for humans to process in real-time to make adjustments and accurate predictions. Luckily, we are not alone. The advancements in Artificial Intelligence technologies have made it possible for businesses to use predictive analytics efficiently, effectively and in real-time. AI technologies are able to put data mining, deep learning, statistical analysis, real-time scoring, predictive modeling, and optimization capabilities to work on big data sets to bring about innovation in long-term decisions as well as day-to-day operations.
With the increase of IoT devices, smart building and smart cities, AI technologies are able to monitor these devices and ensure efficient and effective day-to-day operations. Developer and DevOps Evangelist Daniel Oh says AI technologies can “use this machine data to quickly respond to problems and identify when human involvement may be needed”. It is crucial that businesses harness AI technologies to their advantage in this way. In order to stay ahead of the AI and predictive analytics game, businesses need to hire or train data analysts who understand this AI technology and the application thereof. Villanova University predicts that these up-to-date data analysts “will have an important part to play in the increasing reliance on the speed and efficiency of AI-assisted decision-making and implementation”.
Are You Using The Right Tools?
It’s clear that businesses need to quickly get to work on leveraging the power of predictive analytics to ensure they are operating and moving in the direction of optimum success. In order to do this, these businesses need to acquire the right tools as well as ensure that the tools they have are up-to-date. Today, there is a plethora of quality tools available for the different areas of predictive analytics. It is up to every business to determine which tools to purchase and whether they are suited to do what they need to do.
For instance, businesses that are looking to improve their search engine optimization could use Google Analytics to achieve success. Those businesses that are looking to use big data to optimize their entire business, however, need to acquire more heavy-duty tools. These businesses need to look for big data platforms and big data analytics software. They can start by looking at this list of the top 53 Bigdata platforms and analytics softwares which includes Periscope Data, Microsoft Azure and Amazon Web Service. No matter the need, it is important that businesses do their homework to which of these platforms would suit them best.
It is key to understand the impact of IoT and AI in terms of predictive analytics for business purposes. These three technological developments do not exist in isolation. Rather, they assist each other to further better business decisions and promote long-term success. Simultaneously, it is essential that businesses choose the right analytics tools for their own needs and goals. Considering the vast variety of tools that exist today, businesses must do their due diligence in picking the most suitable tools so as to ensure maximum efficiency. Ultimately, understanding where IoT, AI, and analytics intersect is vital to business prosperity.
Author bio: Ainsley Lawrence is a writer who loves to talk about good health, balanced life, and better living through technology. She is frequently lost in a good book.