Data, analytics, big data, data analytics, and everything related to analytics and business intelligence, are going to be topics of prime focus and importance for companies in 2017. While some companies tried to surprise the market with big data technologies in 2016, the next year is going to see a much more advanced version of the same trend. While the analytics landscape will be dominated by unstructured data, there will be a huge demand for professionals who have a strong command on the analytical tool R.
Companies will strive for analytical agility and automation. Old mechanisms such as A/B testing or multivariate testing will be replaced by real-time agile methods of analyzing data. Organizations will be interested in knowing the best BI solution for their business. They would like to know the best way to analyze data and present insights. Being analytical will no longer matter. Hiring data analytics professionals will not be enough. There will be a demand for data scientists, who only see, eat, breath and sleep data and analytics.
Data Analytics Buzzwords That Will Be in Vogue
Predictive & Prescriptive Analytics Tools
BI Center of Excellence
Collaborative Business Intelligence
Data Storytelling & Data Journalism
Visual Data Discovery
Big Data & Advanced Analytics Pipeline (BAAP)
Latest Data Analytics Trends in 2017
Consumers will be interested in new devices that have sensors to track a lot of data. They want their old devices to be smart enough to track data, like their mobile phones, tablets, or desktops. While 2016 was the year of the wearables and the data derived from them, 2017 could see data being transmitted from drones, flying cars, or even roof tiles. There could be augmented reality eyewear, data from which will be in demand to be analyzed. Accessories could be replaced by "app-cessories".
Dashboards provide a lot of insights into the analyzed data sets. However, in 2017, there will be a demand for simpler and real-time dashboards which can revamp the way insights are created and interpreted. Besides, analytics platforms will not just give insights. They will start giving predictive insights. 2017 will see the rise of interactive business dashboards.
Predictive analytics methods like Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) will be in demand. Artificial Intelligence and machine learning will change the way companies approach analytics and data management. Firms will be interested in advanced analytics technologies based on which complex algorithms will be created. AI will be at the heart at most of these algorithms.
One of the most important data analytics and BI trends in 2017 will be setting up multiple BI and analytics centers of excellence, which will foster adoption of self-service analytics tools and methods. Embedded analytics will be the new capability that companies will look for - analytics tools embedded or integrated within an organization’s existing software and native applications. Some more important analytics trends to look for in the year ahead are -
Move from IoT to Internet of Anything - The Internet of Things (IoT) got quite a few companies hooked onto it, as it dealt with deriving insights from data of various connected systems. Moving a step ahead, this year, the impetus will be on the Internet of Anything, where insights will be derived from “all” the data that is available, from all systems, devices, sensors, server logs and machines available. Data from external sources like servers, sensors, space satellites or oil rigs will also matter, thus making the Internet of anything of great importance and perhaps a new paradigm in 2017
Data Lakes Technology - A data lake is a repository that stores huge volumes of raw data in its native format. In 2017, it could become a common repository for housing unprocessed IoT data or machine to machine (M2M) data. A thing to note here is that the value of IoT data will depreciate very fast. Sensor data could erode faster than transaction data.
Data Warehouses Will Phase Out to the Cloud - While there has been a lot of talk about data warehouses being a thing of the past, there are many companies that are still depending on them. However, the growth of data warehouses has been slow due to the cloud, where most companies are moving their data warehouses to.
Leaders in the cloud data warehouse space, such as Redshift from AWS, Google’s BigQuery, Teradata, and Microsoft Azure will see an increase in user base. Most of the companies that have invested in Hadoop will continue to retain their data warehouses and will leverage the cloud-based solutions depending on the compute resources they have in the warehouse.
Data derived from the different connected systems in the IoT space will lead to an explosion in the volume of data available. Providers of cloud-based data warehouse services will develop solutions which will enable companies to move data to the cloud seamlessly.
Business-user-generated Data - Companies will believe in doing things by themselves, which will lead to the growth of self-service data discovery tools like Tableau, which is known to reduce the time to analyze data. Preparing data for analysis usually takes a long time, given the size and variety of data available. Hence, companies will want to save time and will be interested in data preparation tools like Trifacta, Lavastorm, Paxata, and Alteryx.
Motion Platform Data - There will be a need for data to be analyzed from motion platforms. Companies will aim at developing a high-level platform which can handle multiple device protocols and the data associated with them.
NoSQL-based Schema-less Database - Companies may move on from using operational database management systems like SAP and Oracle to NoSQL-based options like MongoDB, MarkLogic, etc. Some companies will also want to work with Apache Spark rather than work with Hadoop or any other big data and analytics tool.
Mission-critical Workloads - Hadoop, an open-source platform that processes large datasets, will gain significance in 2017. A large number of companies that believe in using traditional methods to analyze data, will use Hadoop to support their mission-critical workloads, especially the enterprise edition of Hadoop.
Hadoop will be in great demand - Companies will opt for the enterprise edition of Hadoop, which will offer capabilities in the areas of data exploration. Apart from Hadoop, enterprises will also be interested in big data technologies like Cloudera, Actian Vector, AtScale, and Jethro Data.
Restoring faith in Hadoop - Companies which have been using Hadoop all these years will try to learn from the mistakes they would have made while working with Hadoop and try to not repeat them in 2017. They will renew their Hadoop deployments, especially in areas like data integration, data security, data governance and data reliability.
Changing the Way Data is Shared - There will be a need to share data across services and companies will make attempts to standardize the way data is shared. There could be a need for devices to communicate in one language or big data technology. Analysis also will have to be performed in a common query language or a universal API perhaps. A format war is predicted in the analytics domain.
Digital Assistants Will Enter Homes - These devices will provide that kind of behavioral data which companies have been trying to acquire for a very long time. These digital assistants will, to a certain extent, define the way you live, eat, travel, etc. and data related to all your day-to-day activities will be recorded. Needless to say, this data will be beneficial to advertisers who want to track every move of their customers as well as potential customers.
Data will be Easier to Find and Manage - Getting access to data from around the globe will become easier. User-friendly data analytics tools will enable companies to extract valuable information and insights from abundant data available.
Data Privacy - As the amount of data will continue to increase, there will also be increased concerns about the security of the same data, especially in countries where there are strict regulations related to data privacy and security. Compliance with the data regulatory laws will become an intrinsic ask for many companies.
Simplifying Big Data - While a lot of companies talk about big data technologies, understanding them has always been a challenge. A lot of areas related to big data technology need to be demystified and there will be a demand for service providers who can do this as well as utilize the technology to deliver results.