Gartner’s Top 10 Data and Analytics Technology Trends for 2019 - Explore What These Trends Mean for Your Business

The global landscape in big data analytics is all set to evolve and transform.

Gartner, the world’s leading research and advisory company, has recently shared the top 10 trends in data analytics for 2019[1] that are going to revolutionize the way businesses function and make decisions over the next 3 to 5 years. Did you know? FORMCEPT’s flagship product MECBot envisages most of these trends and are paving the way for actionable business intelligence at scale. In this post, we share with you these top 10 trends identified by Gartner and the value propositions they hold for your enterprise.

Gartner, Press Release, Feb 18, 2019[1]

   1. Augmented Analytics

Traditional Analytics puts data first and is, therefore, highly dependent on your data team and underlying technology, leading to massive technical debt for organizations. With Augmented Analytics, you can leverage smart data discovery and auto-recognition of patterns at scale and say goodbye to the woes of traditional analytics. MECBot, for example, is built on the principles of Augmented Analytics and puts your business first instead of data.

With MECBot, a business model can be directly created by CXOs or Data Scientists or Data Analysts or all of them collaboratively. MECBot directly pulls the data from the configured sources and maps it to the specified Business Domain-Entity Model. Data engineers can configure MECBot with available sources and provide the details on interlinkages. It provides advanced analytics modules that work out-of-the-box and allows you to build models like loyalty, churn and segmentation without dumping the file or moving the data around. It also keeps your analytics outcomes up-to-date by keeping the underlying view hydrated with the incoming data. MECBot allows you to create a flattened data view for the chosen entities without writing any complex SQL joins.


   2. Augmented Data Management

Contrary to popular belief, it is not the actual application of AI algorithms like Machine Learning & Deep Learning that poses the key problem. The real stumbling block hits businesses, CXOs, data scientists and analysts one step before, i.e. in getting the uneven, disparate and multivariate data cleaned and prepared for these algorithms. This is where Augmented Data Management comes in.

Augmented Data Management encompasses ingestion of high volume of data from internal and external data sources, cleaning and massaging the ingested data for noise-reduction, and pre-processing and seamless unification of heterogeneous data sources into an extensible data fabric. MECBot is the #1 Augmented Data Management Platform which unifies structured, unstructured and poly-structured data from diverse sources into a smart data fabric. It then integrates external knowledge bases to enrich the data and contextualize it and powers user-autonomy through lineage, version control, banking-grade security, compliance and audit.


   3. Continuous Intelligence

Diverse data forms need to be organized and unified seamlessly into a continuous smart data grid that captures and represents the underlying business domain. And, this is where traditional data management loses the battle even before it begins. MECBot’s continuous intelligence keeps your analytics outcomes up-to-date by keeping the underlying view hydrated with the incoming data. MECBot can reuse existing views as well to get you started on the same day instead of waiting for months to get it up-and-running.

The insights generated themselves become a continuous grid of discoveries that keep refreshing in near-real time and keep streaming to you a dynamic picture of the data problem at hand. This smart, continuous insight grid runs in the background and feeds all your business decisions at scale, reducing your time-to-market drastically and creating your very own reliable digital data team that ensures that all your decisions are data-driven.


   4. Explainable AI

Explainable AI refers to the ‘why’ behind AI application outcomes incorporated into the user interface in a natural language in the form of a business narrative. For example, MECBot’s context lattice takes user inputs on the data relationships and his preferences to extract logical datasets that unravel these connections. These logical datasets or the context lattices can be accessed through MECBot APIs. MECBot is now available to the user to slice and dice the data. You can say goodbye to multi-op queries and SQL Queries with our free-form search, explore data with descriptive analytics tools, like range, standard deviation, central tendency, etc., access our fully functional query feature in English language from multiple databases and visualize the data with MECBot’s own visualization tools or using third-party tools like Tableau, Qlik etc.

You can now use pre-built AI models with a single click, such as Customer Loyalty, Segmentation, Clustering, Predictive Analysis, and so on, which are powered by Machine Learning, Deep Learning, and NLP. Since MECBot automates these for you, you do not need the in-house capability to build, run or extract insights from these models.  You can also perform highly specific queries on your data that can further augment your insight pipeline and user dashboard. Enhanced free-flow search can also happen now, like "Customer Loyalty in Bangalore," where loyalty determination is enabled via the Customer Loyalty model.


   5. Graph

Graph data model plays a pivotal role in capturing and preserving the inherent relationships among business entities, which makes it the most comprehensive approach for the creation of smart data fabric for enterprises. This smart enterprise graph then needs to be modeled and purpose-built to turbo-charge pattern detection, free form queries, and powerful AI algorithms. It also needs to be continuously updated with new data, insights and domain relationships such that it can be compressed and decompressed to any degree of granularity.

MECBot automates the entire data unification process envisaging all forms of data at scale, and delivers unprecedented business results. To accomplish this, MECBot first structures the unstructured data contextually using domain specific business ontologies and marries it with structured transactional data in near real time. This creates a comprehensive Data Graph for an enterprise (Smart Enterprise Graph).

This Smart Enterprise Graph is accessible through “Free Form Vertical Search” and APIs. Plus, it is always hydrated through schedules and can be transformed into any shape required by the downward analytics or data application layers. MECBot also comes bundled with cool features like the option to teleport your data, banking-grade security, version controlled data and also OOB ML/DL/AI/Statistical/Graph algorithms that run as functions (FaaS) at Scale using Kubernetes and Dockers.


   6. Data Fabric

With rapid and exponential growth in data variety, velocity and variability, data preparation and management has become an excruciatingly painstaking process. The vast majority of enterprise data today is unstructured, residing outside the organization, thereby shifting the centre of gravity of enterprise data to an external locus. This shift has huge implications. Firstly, external data is rarely clean. It comes in the form of a data ore that is contaminated with substantial noise, making it sparse and non-uniform. It is also largely unstructured. Cleaning up this data through multiple stages of pre-processing is a battle of its own.

Secondly, this external data needs to be married to the organization’s internal data. Here, the challenge is to seamlessly unify external and internal data that are available in diverse and incompatible formats. Merging of disparate data forms into a single, machine-readable and human-retrievable format. Simply put, Data Fabric is the underlying data management framework that allows you to securely store and access data with flexible granular controls, coupled with seamless version control across the entire repository.

By adopting the data fabric approach for your enterprise architecture, you can navigate fluidly across disparate data sources and infrastructure types. With a single, consolidated framework to manage, enhance and connect data powered by mobility across multiple isolated decision centers, you can leverage infrastructure solutions that align with your business needs without worrying about compatibility, integrity or security. Know more about Data Fabric in our blog: Everything You Need to Know About Data Fabric.


   7. NLP / Conversational Analytics

With the booming power vested in social media and voice of customer (VoC), customer conversation is the new currency. In fact, natural language narration and contextual storytelling are so vital and intricately woven into the success of the business, that various NLP products and solutions are now flooding the market.

The catch? The conversation commerce fuelled by NLP and text analytics are not embedded into the enterprise fabric and are still analyzed in silos. MECBot takes care of this by integrating text analytics (MECBot’s INK) seamlessly into its data analysis and augmented data management framework. Recently, we validated MECBot’s NLP cum text analytics integration with one of the leading online insurance policy comparison platforms in India.

Our solution builds insights from text data derived from voice data that is generated when customer service agents handle customer queries. Identification of policies is also done from chat data when agents response online to customer chats. The top policies mentioned in the overall conversation data are then clustered by time, top trends and phrase variations are detected over time excluding the noise, Intent Analysis is carried out based on the data collected and processed, and other features are loaded on top of it, such as chat statistics like total chats, average chat segments, conversation ratios, along with a fully functional keyword search around the policies – i.e. to identify the respective chats in which a searched policy has occurred.  This application is highly useful for Business Executives, Agent Leads and Business Managers to understand the various analysis around the customer queries, agent service, response time, product performance, policy analysis, and overall effectiveness of agent response.


   8. Commercial AI and ML

Gartner has projected that “by 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercial solutions rather than open source platforms.”[1] Commercial vendors with high-end features such as lineage, version control, audit, metadata management, master data management, are now making in-roads into open source technologies as well. MECBot, for example is one of the first Open Cognitive Platforms to encapsulate the full plethora of user control and data management suite traditionally offered by existing commercial AI and ML vendors.


   9. Blockchain

Contrary to popular belief, data fabric is not a mere spatial extension of data warehouses and data lakes. It is a transformative approach in envisioning enterprise data that revolves around the need for a single version of the truth - or at the very least, for only a few compatible versions of the truth[2]. This is where Blockchain comes in. In the years to come, Blockchain technologies are set to be the gold standard in pin-pointing data-source and ensuring a single, uniquely identifiable copy of the data coalesced into distributed architecture and spread across multiple user definitions.


   10. Persistent Memory Servers

Persistent memory is positioned as an in-between memory architecture wedged between DRAM and NAND flash memory. It points to the availability of efficient mass memory at low enterprise costs meticulously designed for dynamic and high-performance workloads. It can drastically shoot up performance scores of diverse applications,  more democratized availability, shortened boot times, smarter clustering and more evolved security protocols, while preventing costs from spinning out of control. Organizations can simplify complex data architectures by adopting de-duplication using Persistent Memory.

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Primary Source: Gartner, Press Release, Feb 18, 2019 “Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019”