Analyzing large and varied sets of data to uncover hidden patterns, correlations, market trends, preferences, etc. better known as data analytics is by no means a new field. It has taken some for organizations to embrace it. But today, with the advent of technology and the availability of more data, it is starting to be widely used by businesses for their growth and development. More professionals are taking data analytics courses to equip themselves with skills and tools that will benefit their organizations and themselves.
10 trends that will shape the future of data analytics have been put together below:
Internet of Things (IoT)
The IoT market is growing at a fast pace and expected to expand 4 times its size by 2023 owing to the further advancements in the fields of data processing and advanced analytics.
Hyper-personalization
The businesses do not need to deliver one product through a hand-picked set of marketing strategies anymore. Using data analysis, they get in-depth and accurate knowledge about customer personas, behavior, preferences, etc. and understand customer needs much better, thereby, being able to tailor products and/or marketing strategies to suit the customer. More brands are embracing this for their success.
Artificial Intelligence (AI) and Machine Learning (ML)
AI and ML are being adopted by businesses extensively to analyze big data about various aspects of their functioning and strategize accordingly for better outcomes. This is especially true in the case of improving and providing a seamless customer experience.
Augmented Analytics
It is being adopted by organizations to use the power of machine learning to automate data preparation and presentation, and to produce rapid outcomes in data-driven domains.
Predictive Analytics
It is being widely embraced by organizations to solve problems in an insightful and structured manner. Organizations are using this tool for forecasting future behaviors for greater profitability, minimize risks, improve business operations, etc.
Cloud services
It offered by various providers and platforms have eased business concerns about handling and storing today’s big data. This technology is here to stay.
Edge Computing
It has solved connectivity and latency challenges associated with data travel and has revolutionized technology in this age of IoT-enabled smart devices. Edge computing will consolidate its position with greater usage of drones, wearable technology and autonomous vehicles.
Behavioral Analytics
It is currently being used extensively by organizations for personalization, customer intelligence and marketing. However, efforts are on to explore more ways of using behavior analytics especially in smart city projects, traffic pattern identification, track medical shipments and cold storages for breaches, etc.
Graph Analytics
This technology is used to map relationships in big data as well as finding the strength and direction of such relationships. There is a strong case to apply it to areas such as detection of financial crimes, conducting research in bioinformatics, logistics optimization, etc.
Blockchain Technology
With the success of cryptocurrencies which use blockchain technology, data scientists and business organizations (especially financial institutions) are considering merging big data with blockchain technology to expedite processes and create better fraud detection mechanisms.
The big data boom has brought with it the deluge of data and an overarching requirement from corporates to invest in it. Business investments of any kind of demand ROI. The return on investment that businesses seek from the huge database they collect, is twofold. They want the data to provide key insights that can be turned into a competitive advantage for the business and they want this competitive advantage to translate into revenues.
With data collection pegged to rise by 4300 percent by 2023, companies need the data to be more accessible and useful. There are several companies still trying to make head or tail of the enormous data they have. This can be sorted by streamlining the data collection and analysis process. If the process is in alignment with the strategic goals of the company, the overall efficiency and productivity can increase, leading to an increase in revenues. Here are 5 ways to do so:
Enhance data access within the organization: All teams in the organization need business insights and information in real-time to be more productive. Traditionally, the data is available with the data analysis team. The top management must ensure the access of this data for all teams. By creating internal knowledge and data platforms and providing access to all teams across the organization, productivity, efficiency and revenues can all be positively impacted.
Increase customer engagement with cognitive computing: Cognitive solutions which mostly refer to artificial intelligence are changing the way businesses interact with their customers. Several sectors such as banking, retail and healthcare are already deploying cognitive solutions to engage with their customers. Using chatbots enabled with natural language processing, you can capture data and real-time insights from customers.
Use hybrid data sources: While cloud computing is great, it comes at a substantial cost and requires a lot of effort and time for creation. Every company may not be ready for it. However, using a variety of cloud services is possible and affordable for all. Using different cloud sources can help link internal and external data to aid in better analysis and produce more comprehensive insights.
Make use of all the unstructured data: There are innumerable data touchpoints in today’s time. You get data through POS, emails, chats, documents, social media, transcripts from the call centre, customer feedback and industry reports. By linking all this data through software and using an analytics tool, insights and trends can be drawn and understood far better. In fact, insights from unstructured data can help determine barriers to product development and help enhance product design, services, and reduce churn.
Start small and scale up slowly: It is important to ensure that you can scale up your data initiatives. The big data initiatives of as many as 55% of organizations fail. Hence, big data must be utilised efficiently to enhance revenues sufficiently to scale up the initiative. It may not be possible for all businesses to start big, but starting with the deployment of cloud services and slowly building big data capabilities is possible.
Using the above ways can help organizations utilize big data to its full potential. Big data analytics courses can help young professionals or those aspiring for a career in analytics. More companies want professionals with expertise in big data analytics as they look to extract the full potential of their big data.
More Information:
What does a career in Data Analytics look like?
How to Build a Successful Career in Data Science?