There is too much noise around Data & Analytics and not without good reason. There is a huge amount of customer data being collected, processed and hopefully turned into value driven insights. Globally we are witnessing a consolidation of analytics capabilities across the technology world, from Salesforce acquiring Tableau, Microsoft having its Power Platform. All this leading to real-time processing of data ensuring more effective analytics and result-oriented actions.
Digital data worldwide is expected to quadruple to 175 zettabytes by 2025 from 40 zettabytes in 2019. This makes data management and analysis challenging. It’s also instrumental in ensuring many of the trends that are likely to be prevalent in 2020. Gartner states that by 2020, more than 40% of data science tasks will be automated. Imagine how much more efficient we can become if we are able to clean-up of all incoming data, create automated weekly reports, do multivariate testing and analysis? And do all of this automatically. There is no doubt in my mind that automation will become all-pervasive and will be a central theme in all aspects related to Data and Analytics. Let’s have a look at some of these trends that are likely to impact us in 2020:
- From data prediction to data prescription:
We have moved from ‘descriptive analytics to prescriptive analytics some time back. Predictive analytical capabilities, using a number of statistical modelling techniques to provide insight into the possible outcomes of business decisions are now staple diet. Going forward we will witness a new generation of “prescriptive” data analysis software that goes beyond providing information and insight to offer recommended courses of action based on analytical results. Prescriptive analytics uses a range of technologies including artificial intelligence and machine learning, computational modelling, neural networks and graph analysis.
- Augmented analytics:
Organisations will want to empower more and more teams with the gigabytes of data- across business intelligence, data science, analytics, and machine learning that they have collected to enable them to take more business value generating decisions. “It’s really about democratizing analytics and getting insights in a fraction of the time with less skill than is possible today.” according to Rita Sallam from Gartner.
- Augmented data management:
Augmented data management will work on tasks like schema recognition, capacity, utilization, regulatory/compliance, and cost models, etc. This will help drive the organizations’ ability to analyse dynamic data with a higher level of automation and the best part is that it will be close to real time. According to Gartner by 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management.
- Connected devices will drive demand for IoT analytics:
The IoT industry is expected to reach $1.29 trillion in 2020. This translates into complex data sets and in great volumes of data. This will demand unique tools and skill sets that companies don’t possess as of now. Companies will use consumer data from (of course, with their permission) apps, fitness trackers, appliances and vehicles. IoT analytics will offer insights on how consumers use products. This will drive personalisation and greater customer engagement and customer experience.
- Natural Language Processing (NLP) & Conversational Analytics:
NLP and conversational analytics are highly complementary with augmented analytics. Most people making decisions do not know SQL. This will provide non-data experts with a new kind of interface into queries and insights. By 2020, according to Gartner, 50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated. Another important feature that is emerging feature is conversational analytics, which will let you drill down with more specific questions. Until recently, it’s all been about visualization. Conversational Analytics will add another dimension to the insights.
- Commercial AI/ML will rule the market:
Open source has been a big driver of big data and AI and machine learning. Most organizations have been around for some time and do not come from a digital first background. These companies have run AI and ML projects but have been unable to scale their projects. Gartner states that by 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercial, instead of open source, platforms.
- Localised data strategies will become a business imperative:
With the likelihood of the Indian Data Protection Bill 2019 to be passed in this winter session of parliament, data localisation is set to be a key trend next year. Companies that have factored that into their plans will have a huge head start. Privacy regulations such as GDPR, California Consumer Privacy Act (CCPA) have already had a huge impact in Europe and the US. This is likely to force businesses to adopt localised data privacy strategies involving regional data residency programs. What this means is that personal information will be stored in a specific geography where that data is processed according to local laws and regulations.
- Data fabric to drive integration:
This trend is tied closely to augmented data management. It allows organisations to support agile data at scale. With data becoming more siloed and unevenly distributed it has become a huge challenge. Data fabric is designed for data residing in silos. A logical data warehouse architecture enables access and integration of data seamlessly across heterogeneous storage. Gartner states by 2022, custom-made data fabric designs will be deployed as static infrastructure.
- Blockchain:
Blockchain has been ubiquitous for some time now. It certainly is a trend across many technology areas. It is important in data and analytics in the area of trust. It is about cryptographically supporting immutability across a network of trusted participants. Blockchains ability to track if something has changed is priceless. Gartner forecast by 2021, most private and permissioned blockchain uses will be replaced by ledger DBMS products.
- Data as a Service:
Businesses and organizations are increasingly providing access to data and digital files on an as-a-service basis, either internally to departments or to third parties as a revenue generator. Data as a Service utilizes cloud technology to provide people and applications with access to data no matter where the people or applications may reside. Many organisations are thinking on the lines of bundling data with BI tools to provide subscription services.