Testing

Category: Augmented Analytics

  • How does Augmented Analytics aids in promoting new roles?

    How does Augmented Analytics aids in promoting new roles?

    From our personal data to the unfathomable depths of data sources, we are surrounded by a humongous amount of data. Every organization, starting from small to big organization, make use of the data which helps in achieving business outcomes. Some organizations use forecasting techniques based on traditional mathematical and statistical models, while others use predictive analytics that uses AI/ML techniques and other advanced analytics to predict/forecast the future. One such example is the prediction and prevention of driver churn in the company. So there is a need for a specialized predictive analytical solution to help companies run their business better. But these solutions require expert data scientists, who are expensive to hire and are scarce in supply. Thus, this has led to the emergence of citizen data scientists.

    The emergence of Citizen Data Scientists

    The emergence of citizen data scientists is happening for two reasons. First, they are proving to be a strong-complement-and cost-effective to expert data scientists, who are typically scarce in supply and expensive to hire. Second, data science is getting simple with Augmented Analytics. According to Gartner, “the use of new analytics and business intelligence tools are extending further into the enterprise.” Nowadays, many organizations have started using Augmented Analytics. It democratizes insights from analytics, including AI, to all business roles. It makes data science and ML/AI model building accessible to new citizen data science roles (business analysts, developers, and others). It will make existing expert data scientists more productive, freeing them for high-value tasks.

    By 2020, the number of citizen data scientists will grow five times faster than the number of expert data scientists. Gartner predicts that, by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity. Gartner also predicts that by 2024, a scarcity of data scientists will no longer hinder the adoption of data science and machine learning in an organization.

    Who are Citizen Data Scientists??

    Gartner defines the citizen data scientists as a person with emerging capabilities, who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. They are “power users” who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Citizen data scientists provide a complementary role to expert data scientists.

    Typically, citizen data scientists do not have coding skills but need to develop strong domain expertise to understand the data. They can also build models using drag-and-drop tools, run pre-built data pipelines and models. They do not replace expert data scientists, as they do not have specific advanced data science expertise to do so. But they certainly bring their own business expertise and unique skills.

    The citizen data scientist is a role that has evolved as an “extension” from the other roles within the organization. Their role will vary based on their skills, domain, and interest in data science and machine learning. Roles that filter into the citizen scientist category include:

    • Business Analysts
    • BI Analysts/Developers
    • Data Analysts
    • Data Engineers
    • Application Engineers
    • Business line manager

    Developer data scientists, a type of citizen data scientist, is a significant development driven by augmented analytics. These are application developers armed with augmented data science tools, who can build ML and AI models to embed in their application. This will relieve the intense demand for expert data science skills and offer opportunities to upskill existing application developers.

    Empowering the Citizen Data Scientist

    As there are not enough qualified data scientists to meet the demand for data science and machine learning skills, citizen data scientists emerged to provide their unique capabilities to extract predictive and prescriptive insights from the data. This growth enabled by augmented analytics, will complement, and extend existing enterprise applications. It is also available to a broad range of users such as business analysts, decision-makers, etc. across the organization. This will drive new sources of business value. Citizen Data Scientists must collaborate with a specialist data scientist to gain necessary skillsets. Also, the organization must implement an upskilling program for developing citizen data scientists from existing roles. In addition to that, it is also important to ensure a data-driven culture across the company to increase their acceptance and bring about change amongst employees.

    Citizen Data Scientist will change the workflows

    As mentioned before, citizen data scientists are empowered to do their own data analysis, build models using drag-and-drop, and without any prior coding experience. This will allow them to make decisions based on what they find. Citizen data science eases the burden on existing expert data scientists and analysts, making them more productive and collaborative and freeing them for high-value tasks such as model building, validation, testing, delivery, and operationalization. Business roles can get quick returns on their data-based questions which increases efficiency.

    The rise of citizen data scientists is enabled by augmented analytics. It is designed for business users instead of a technical audience. Advancements, like natural language processing (NLP), is one of the most important factors for a non-technical user. Instead of writing SQL queries to extract the data, NLP uses a natural language query (NLQ) to ask a query in plain text and generates the results in a natural language.

    Embracing augmented analytics in an organization as a part of digital transformation strategy helps in building trust and delivering advanced insights to a broad range of users including citizen data scientists and, ultimately, operational workers without expanding the use of data scientists. And by incorporating the necessary tools & solutions and extending resources & efforts, enterprises can empower citizen data scientists!

    Are you leveraging Citizen Data Scientists within your organization? If yes, who are they according to you, what are their titles, and what do they do? I’d like to hear your stories in the comment section.

    Learn how augmented analytics can help transform your approach to enterprise AI

    Schedule a demo

  • The Benefits and Challenges of Augmented Analytics

    The Benefits and Challenges of Augmented Analytics

    Augmented analytics uses AI/ML techniques to automate the tedious manual data preparation processes, insight data discovery, and sharing. It also automates data science tasks such as ML model development, management, and deployment. Traditional BI tools have supported basic capabilities for joining, manipulating, and transforming the structured data. While augmented data preparation streamlines processes for data profiling, manage quality, cleaning data, modeling, and labeling metadata in a manner that supports reuse and governance.

    Augmented Analytics includes Natural Language Processing (NLP) and conversational analytics, which enables less technical experts such as citizen data scientists to interact with the data and insights to give recommendations for the business. By 2020, 50% of analytical queries will automatically be generated via NLP or voice. This ease of access promises many benefits, but the organization faces several challenges in making it work well in practice.

    Benefits of Augmented Analytics

    1. Faster Data Preparation

    Data Scientists and data engineers spent around 80% of their time on manual data preparation. Augmented Data Preparation uses AI/ML automation to bring data together from disparate sources much faster. Algorithms replace manual processes and automate the data cleaning process in a fraction of time. It is also used to detect schemas, joins, repetitive transformations, and automatically recommend associations between disparate data sources, enhancing productivity and efficiency.

    2. Improved Data Literacy

    As organizations continue to collect a massive amount of data, it is essential that everyone, regardless of expert analytical skills, can gain value from the data. Democratizing AI across the data value chain promotes data literacy by automatically surfacing and explaining insights using natural language, making a recommendation, and empowering all users to confidently take action. This assists in fostering a data-led culture that benefits the organization for the long term.

    3. Reduced Analytical Bias

    Analysts always make assumptions for finding answers when they do not know where to start. Often those assumptions could lead to the use of specific data to support it. Augmented analytics can help minimize potential bias by performing automated analysis across a large dataset of statistical importance. It reduces the risk of missing important insights.

    4. Reduced Time to Insights

    With augmented analytics, instead of an analyst manually testing all the combinations of variables in the data, ML algorithms for detecting correlations, segments, clusters, outliers, and relationships are automatically applied to the data. Only the most statistically relevant results are presented quickly via smart visualizations, which are optimized for the user’s interpretation and action.

    5. Democratization of Analytics

    Augmented Analytics will democratize insights from analytics, including AI to all business roles. It will make data science and AI/ML model building accessible to new citizen data science roles such as business analysts, developers, and others, etc., while making expert data scientists more productive and collaborative and freeing them for high-value tasks.

    Companies that have implemented augmented analytics has seen positive business outcomes such as an increase in decision making by 51%, an increase in efficiency by 50%, and an increase in effectiveness by 48%. Although not all companies have realized these benefits yet, over 90% of decision-makers expect to realize these benefits as analytics capabilities continue to improve.

    Challenges of Augmented Analytics

    Augmented Analytics is the next wave of disruption in the data and analytics market and must be adopted to build and sustain a competitive advantage. But it requires the right balance and alignment of strategy, people, process, data, and technology components. However, efforts to incorporate it will likely encounter resistance as follows:

    Adoption

    One of the adoption challenges faced by the organization is the outcome associated with it. The organization expects favorable and quick results. But augmented analytics has a lifecycle of its own. It uses AI techniques to do any form of computation, analytics, and automation. Once implemented, it will take time to mature, improve its analysis and outcomes over a period. The other potentially damaging challenges are as follows:

    • The prevalent “black box” image of augmented analytics is not transparent in terms of decision making.
    • There is a lack of trust in the displayed recommendation and insights unless a specific explanation is provided.
    • There is a dependency on legacy analytics platforms that act as a barrier to adopt Augmented Analytics.
    • There is a threat to job security because of automation in every aspect of analytical operations and the revamping of workloads and work processes.
    • There is a resistance from the business leaders to adapt to changing scenarios and rely more on intuition and traditional decision-making practices.
    • There is a misconception that augmented analytics maturity is linearly progressive and implemented once a robust foundation is established.

    AI Governance

    Another important challenge is the need to emphasize AI and analytics governance and collaboration between analysts and data scientists. The adoption of Augmented Analytics will require existing organizational models to evolve and support a growing footprint of citizen data scientists embedded within the business units. It will not only yield sizable returns but, if left unchecked, will also have adverse effects. Therefore, data and analytics leaders must outline the rules to govern the use of analytic content and insights created as outputs of augmented analytics as well as data science tasks to ensure the accuracy, validity, and bias levels of findings and recommendations.

    The innovative functionalities of augmented analytics demonstrate a variety of applications across all sectors of the economy and promise to transform the digital landscape by streamlining business operations and increasing access to useful data.

    Is your organization planning to adopt Augmented Analytics? If yes, then what are the challenges your organization is facing? Feel free to share your thoughts in the comments section.

    Learn how augmented analytics can help transform your approach to enterprise AI

    Schedule a demo

  • Augmented Analytics: The future of data analytics

    Augmented Analytics: The future of data analytics

    One of the latest data and analytics trends which has gained considerable traction these days is Augmented Analytics. The term was coined by Gartner in 2017, goes well beyond the world of data and analytics, bringing in the wave of disruption in the market. By leveraging AI/ML techniques, it transforms how analytics content is developed, consumed, and shared.

    There are compelling reasons why organizations must adopt augmented analytics technology. Many organizations have realized the importance of Big Data and its role in decision making across the business. However, this sheer volume of data available to organizations is making effective interpretation a real challenge. According to Forrester Research, less than 0.5% of all data is ever analyzed and used. While a mere 12% of enterprise data is used to make decisions. This will only make it worse with the growth in IoT connected devices which is expected to generate 79.4 Zeta Bytes (ZB) of data across 41.6 billion devices, according to IDC Forecast.

    To date, many processes remain largely manual and prone to bias across the data value chain. This includes managing and preparing the data for analysis, building ML/AI models, interpreting the results, and making insights actionable.

    Using the current analytics approach, business users find their own patterns, and data scientists build and manage their own models. This results in exploring their own hypotheses, missing key findings, and interpreting incorrect conclusions. This will adversely affect decisions, actions, and outcomes. According to Forrester Research, only 29% of organizations are successful at connecting analytics to action.

    Augmented Analytics promises to ease this bottleneck. It democratizes AI across the data value chain. It automates the data preparation process, key aspects of data science, and ML/AI modeling using ML (AutoML) techniques and narrate relevant insights using NLP and conversational analytics. It includes:

    • Augmented data preparation uses AI/ML automation to accelerate manual data preparation tasks like data profiling and quality, enrichment, metadata development, and data cataloging, and various aspects of data management like data integration and database administration.
    • Augmented data science and machine learning uses AI/ML techniques to automate key aspects of data science such as feature engineering and model selection (AutoML), as well as model operationalization, model explanation, and model tuning.
    • Augmented analytics as a part of BI platforms embed AI/ML techniques to automatically find, visualize the data and narrate the relevant findings via conversational interfaces, including natural language query (NLQ) technologies, supported by natural language generation (NLG).

    This leads to an increase in productivity, efficiency, and smart decision-making across the organization. One of the greatest benefits of augmented analytics is that it democratizes data analytics for less business-savvy users i.e., Citizen Data Scientists without any specialized training or skills in data science or analysis. Augmented Analytics also enables the adoption of actionable insights for the executive team across the organization.

    So, every organization will need an augmented analytics platform to connect disparate and live data sources, find relationships within the data, create visualizations, and help human users effortlessly share their findings across the entire organization. It will change how users experience analytics and BI and the world by serving up insights that humans could ever imagine.

    Did your organization adopt Augmented Analytics? If yes, how it has benefited the organization? Feel free to share your thoughts and some interesting statistics about augmented analytics in the comments section.

    Learn how augmented analytics can help transform your approach to enterprise AI

    Schedule a demo

  • Evolution of BI Platforms

    Evolution of BI Platforms

    We live in an era of Big Data. Around 1.7MB of data is created every second by every person. 2.5 quintillion bytes of data are produced by humans every-day. This sheer volume of data has become so huge, complex, and fast-moving, that to make sense of this vast amount of data is a challenge. So, Business Intelligence (BI) tools are used to analyze this humongous amount of data to uncover the insights that are crucial for the business. It makes data of any kind, easy to digest with stunning visualizations, detailed historical analysis, and customizable reports.

    Over the decades, BI technology has evolved, and the market shows no signs of slowing down. While the inherent meaning has remained the same, but BI as a set of processes, technologies, and tools has changed a great deal, right from Traditional BI to AI-powered BI which uses Augmented Analytics. Before understanding how augmented analytics will change the analysis and business intelligence process, let us have a look at the evolution of business intelligence.

    Traditional BI

    The first generation of BI technology often referred to as “Traditional BI” was a centralized guardian tool for all enterprise data largely owned and driven by the IT and data specialists. Legacy deployments of multiple components such as data marts, data warehouses were technically complex and required extensive IT staff to maintain and manage it. The Extract Transform and Load (ETL) paradigm integrated data from disparate sources into a central repository for storage. Once stored, data was normalized and structured before it is further utilized to run queries and retrieve data for reporting.

    Ultimately, the IT department generates and delivers static reports to the business owners. The analysis was usually descriptive and performed by specialized data analysts with restricted access to the reports. This entire process could take days, weeks, or even months to produce insights due to dependency on skilled IT staff. And thus, unable to make timely data-informed decisions. To make BI more accessible to business users, self-service BI became the next generation of analytics and BI.

    Self-Service BI

    The main drawbacks of traditional BI were the need for highly skilled technical analysts, lengthy time-to-insights, and poor quality of the data being analyzed. These drawbacks were overcome by a more agile approach that favored self-service capabilities: Modern Self-Service BI. This eliminated the technical stack designed for IT users and focused on providing data discovery and visualization tools to business users. It also provides business users the ability to conduct ad hoc analysis of data from disparate sources without any advanced technical skills. As compared to traditional BI, they can handle larger volumes of data drawn from multiple sources allowing for deeper analyses. They replaced the rows and columns of traditional data presentations with graphical pictures and charts.

    In addition to historical reporting, it provides predictive and prescriptive reporting and insights in real-time. With these tools’ users get the information to make better decisions, with greater ease, and without having to rely a lot on data analysts and IT professionals. Modern BI solutions also make data governance, security, and access control simpler for IT teams.

    Need for an AI-powered BI tool

    Despite being more insightful and easier-to-use than traditional BI, self-service BI tools do have few limitations. As the volume of data rises, there is a requirement of data scientists to make sense of huge datasets. But scarcity of data scientists and manual data preparation makes the process highly inefficient and prone to error. Also, the insights provided by self-service BI systems are limited to the type of queries made by business users. This is where the need for a new AI-powered BI i.e. Augmented Analytics BI system arises. It not only automates the data preparation tasks but also parts of data insights and the data discovery process.

    Augmented Analytics BI

    Augmented Analytics integrates AI into the analytics and BI process to help the user to prepare their data, identify relationships within the data, discover new insights, and easily share them with everyone in the organization. It reduces the dependency on highly skilled data scientists by automating insight generation using machine learning and artificial intelligence algorithms. Gartner states that more than 40% of the data scientists’ roles to be automated by 2020. Augmented Analytics BI tool can help less technical experts like Citizen Data Scientists to provide recommendations and suggestions based on their domain and primary skills to understand and gain insights from the trends and patterns. It will be free of human biases and reveal hidden insights crucial for the business. Also, the use of Natural Language Generation (NLG) can enhance the BI reporting process by allowing users to query the system and present the insights narratively.

    Augmented Analytics will help move organizations beyond the dashboard paradigm to a new way of consuming insights i.e. data story. These will help the user understand just the insight and context that they need at the right moment to make the decision. By 2025, 75% of the data stories will be automatically generated using augmented analytics techniques.

    Every organization will need an augmented analytics platform to create visualizations, aid in storytelling and then help users to effortlessly share their findings across the entire organization. This will boost the adoption of Augmented Analytics BI across teams, especially among non-technical users. Augmented Analytics will change how users experience analytics and BI.

    Does your organization still use Traditional BI and Self-Service BI? Do you plan to adopt Augmented Analytics BI in future? If yes, then how it will benefit an organization. Feel free to share your thoughts in the comments section.

    Learn how augmented analytics can help transform your approach to enterprise AI

    Schedule a demo

  • Augmented Data Management: Its Importance and How it Transforms an Enterprise

    Augmented Data Management: Its Importance and How it Transforms an Enterprise

    In the digital era, data is at the heart of an enterprise. So, managing the data has become the topmost priority of any organization today. As the data volume increases at a 10X rate, this data growth impacts many organizations in the form of the multiplication of in-company heterogeneous storages, remote sites, and cloud storages. This will require moving the data which results in time, infrastructure, and cost constraints. So, the need for a consolidated data management solution arises which uses AI automation to reduce time and infrastructure costs across the data value chain.

    As enterprises are increasingly standardizing on augmented analytics, it brings together two distinct worlds of data and analytics. This collision enhances interaction and collaboration between the two worlds shaping the associated paradigm in the market i.e. augmented data management. Augmented data management (ADM) utilizes AI/ML to automate manual data management tasks allowing highly skilled technical resources to focus on high-value tasks. According to Gartner, by 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service management.

    This emerging trend is impacting enterprise data management disciplines like data quality, data integration, metadata management, master data management, and database management frameworks, “self-configuring and self-tuning”, as indicated by Gartner. The innovation is changing the data management landscape and the roles of data professionals.

    Traditionally, data scientists used to spend 80% of their time cleaning the data through the extract, transform, and load (ETL) process. By automating the process of ETL, data scientists spend more time thinking about the implications of the data, deriving insights from it, and proposing recommendations to help the business. Organizations will no longer require candidates with experience in statistics and mathematics background to do BI.  They will be able to hire less technical people who understand the business, derive insights, and offer recommendations based on delivered data. A new role like citizen data scientist is also gaining traction these days in an organization to fill the data science and machine learning talent gap caused by the shortage and high cost of expert data scientists. It also makes expert data scientists more productive and collaborative, freeing them for high-value tasks.

    Implementing ADM assists enterprises in organizing and maintaining data quality through cutting-edge technologies, resulting increase in effectivity and productivity. Below are a few points of how augmented data management transforms an enterprise:

    • According to a survey, data scientists spend 80% of their time in manual data preparation. ADM automates routine tasks such as cleaning, profiling, labeling, classification, and tagging. This enables insights to be derived from an organization’s data at an impressive scale.
    • Enterprise leverage advanced analytics techniques such as anomaly detection and correction, imputation of missing data, etc. instead of statistical profiling which creates inconsistencies in data. This will enhance data quality and empower faster, more scalable, and better business decisions.
    • Metadata management involves managing data lineage. If not managed properly, silos of inconsistent metadata will be created in an enterprise providing conflicting information and users will not be able to trust the data. By automating the metadata management process like data cleaning, integration, and attribute matching the data lineage is fully traceable and gives a clear picture of the information contained within the data, the purpose it serves within the organization, and improves data governance. So, ADM converts metadata to a “powerful dynamic system”.
    • ADM automates some of the data professional’s routine manual tasks such as database performance tuning, hardware configuration, and optimization, and other database administration jobs that are computationally intensive and iterative. This reduces errors on deployments, improves reliability, and increases the speed of implementing changes.
    • When data is integrated from multiple sources, inconsistencies in the data occur because of statistical methods based on data matched on names and attributes. By automating this process, more accurate suggestions are made during data mapping and empowering the team to add more data sources highly confident that quality will not be compromised.
    • ADM utilizes ML tools or AI intelligence bots to present smart recommendations but still relies on human skills to make decisions.

    Apart from the aforementioned benefits, augmented data management can also facilitate data for real-time analytics. Startups, where resources are deficient, are also leveraging ADM and harnessing data across several departments, improving collaboration for accomplishing different tasks, and making proactive decisions within their departments. This will eliminate data silos, thereby, decreasing the cost of business operations.

    Enterprises need to revamp the data management process to stay competitive. By leveraging ADM, it automates the manual data management process and provides faster actionable insights without wasting time or resources. It improves the productivity of data scientists, freeing them from the mundane tasks of data management. This enhances effectiveness, saves costs, and improves the revenue generation of an organization. Therefore, deploying ADM practices is the way forward to stay ahead and give blue-chip companies a run for the money.

    Do you agree augmented data management will transform an enterprise? If yes, then how it will benefit an organization? Feel free to share your thoughts in the comments section.

    Learn how augmented analytics can help transform your approach to enterprise AI

    Schedule a demo

  • Emerging Trends in Data Management

    Emerging Trends in Data Management

    As data is generated at an exponential rate, companies are awash with data, struggling to unlock its true value. Data is not only increasing in volume but also in complexity. In terms of ‘Global Datasphere’, it is estimated that by the end of 2019 it stood at 4.4 Zeta Bytes (ZB), up from 2.7 ZB in 2017. In addition, the growth in connected IoT devices is expected to generate 79.4 ZB of data across 41.6 billion IoT devices, according to IDC Forecast.

    As organizations find their way through analyzing the data, it has become critical to have a data management system that can help solve challenges of data integration, silos, manual data management and governance throughout the organization. This can lead to challenges such as increased costs, threat to data security, prone to error and subject to bias.

    If the company wants to continually evolve and stay ahead in the competition, it must have a data and technology-centric approach to Data Management. According to the survey, only 25% of companies feel like they are exactly where they want to be with corporate data management. The new emerging trends democratizes an entire data management value chain. Some of these trends are:

    1. AI-enabled Data Management

    The combination of Data Management systems and AI are synergistic in nature. When AI becomes embedded throughout the data management system, it has potential to impact an entire data value chain. AI-enabled data management helps in automating repetitive and complex tasks. It improves the performance, accuracy, and productivity in an enterprise.

    Data scientists spend around 80% of their time on manual data preparation, feature engineering, and model selection. So, Augmented Data Management utilizes AI/ML capabilities to automate the manual data management tasks allowing highly skilled technical resources to focus on high-value tasks where AI is less mature. According to Gartner, by 2022 data management manual tasks will be reduced by 45% through augmented data management.

    Augmented Analytics democratizes AI across the whole data value chain to automate data preparation process, key aspects of data science and ML/AI modelling using ML (AutoML) techniques and narrate relevant insights using NLP and conversational analytics.

    Even today, organizations rely upon historical databases instead of real-time data for analysis. So, there is a need to collect, index and analyze all data in real-time.  Continuous Intelligence is a seamless AI-driven solution that allows the companies to automatically integrate continuous and insightful data from disparate sources. It completely changes the time-consuming data wrangling process performed in Big Data. Backed with AI, ML, and right training data it minimizes human intervention throughout the process. According to Gartner, by 2020, more than half of the major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

    2. Semantic Data Catalog

    As data is integrated from disparate sources, it is difficult to make efficient use of siloed data sources to easily access, interpret, and track data and its history. Semantic Data Catalog leverages knowledge graph model encoding a Semantic layer that map relationships and describes the data in its business context while integrating data from disparate sources. When linked with self-service tools, it will help data stewards and business users to prepare datasets and curate the data. It is quite necessary in data management initiatives like improving governance, data lineage and data quality as well.

    3. NLP and Conversational Analytics

    Until recently, it has all been about visualization of data which requires highly skilled users, but with NLP/Conversational analytics, it will allow users to ask questions about the data using NLQ search, as well as receive visualization of data and an explanation of insights supported by NLG. Natural Language Query (NLQ) interact with the data reducing technical and analytical query expertise necessary by mainstream business users. By 2020, 50% of analytical queries will be generated via search, natural language processing or voice, or will be automatically generated, according to Gartner.

    4. Data Fabric

    Data is available in a variety of formats which is distributed across multiple on-premises locations as well as hybrid and multi-cloud. As organizations are using many applications, their data becomes increasingly siloed and inaccessible. To provide single view of all the data, data fabric is used. It provides a single environment for accessing, collecting, and analyzing the data making an enterprise extremely agile and eliminating silos. It enables data ingesting, integration, quality, governance and sharing, by eliminating multiple tools and providing faster access to data.

    With the advancement in data collection and storage methods, the future of data management is autonomous. To stay ahead of the competition, companies are compelled to keep up with new and innovative technologies. By doing so, companies become more agile enabling greater access and self-service, more cost- efficient by 38%, gain faster insights, gain better understanding of the data, enables informed and accurate decisions by 33%.

    How does your organization tackle data management challenges? What are the plans to deal with it? Did your organization adopt any of the new trends for managing data? Feel free to share your thoughts in the comments section.

    Learn how augmented analytics can help transform your approach to enterprise AI

    Schedule a demo