Our data universe has witnessed a colossal year-on-year growth of 42.2% worldwide1. IDC predicts that the Global Datasphere will grow from 80 Zettabytes in 2022 to 175 Zettabytes by 20252. With such growth also comes the responsibility of making information easily accessible and interpretable across a range of users that includes customers, business partners, and employees.

Business leaders often speak about the lack of value realization from data analytics projects. They understand that technology enabled innovation opens a world of opportunities, but business benefits are elusive owing to poor enterprise data awareness and lack of adoption.

While this problem is common, businesses are yet to focus on data literacy as a fundamental skill at their workplaces. In the absence of data literacy, enterprises often encounter challenges like miscommunication between departments owing to different data definitions, risk of data breaches, and untapped business improvement opportunities – despite having technical capabilities in their portfolio.

You must recognize that not only data and analytics skills, but also access to the right tools and quality data along with a supportive culture will together help build a data-first mindset. Finally, to permeate the culture across all levels in the organization, it is essential to provide the right change management interventions to nourish data literacy initiatives over sustained periods of time.

With our experience in helping our clients improve data literacy within their enterprises, we have identified six essential factors that organizations should consider as they go about data literacy initiatives:

  1. Assess your data literacy score – Poor understanding of data is most often attributed to knowledge gaps. So, in defining your business case to launch a data literacy campaign, get a baseline of the current knowledge levels and skill gaps in your workforce. In addition, quantifying data literacy levels helps construct your program design aligned to your organization culture.

    Not every style of training is suitable across age groups or varying functional needs. While working with an APAC luxury retail client, Infocepts was able to identify the training needs within each department, translated that to a pilot program and then expanded the model to a multi-country rollout.

  2. Ensure executive management sponsorship – Driving change management isn’t easy; it’s no different in ushering culture change pertaining the usage of data. To orchestrate full support from C-suite management at the start of your program it is essential to articulate the following, rather than focusing on platform capabilities alone:

    • The business case – a clear visibility of value expected to be delivered and against which organization wide investments are required
    • Roles and responsibilities – it is imperative to establish the bandwidth required to launch and sustain the program, for both key roles and other stakeholders
    • Benefits realization – we need to communicate well on how the performance metrics will shift across departments (e.g., turnaround time improvement for customer service)
    • Committed timelines – the program should be designed to deliver quick wins, rather than accrue value at the end of long-time intervals.

    It’s also critical to convey that data literacy won’t thrive simply by purchasing new software or a having a large team of specialized data scientists address all the needs of a workforce numbering in the thousands.

    While engaging with one of our client’s, a global bank, Infocepts identified poor data quality as one of the limiting factors for adoption and literacy among users due to lack of trust in data. Fixing this problem required executive interventions to prioritize data quality improvement initiatives across functions in addition to helping users adopt self-service through focused data literacy programs.

  3. Data visualizations empower literacy – One size doesn’t fit all when it comes to evangelizing the language of data across a large user base. A quick win is to teach users the power of data visualizations, enabling them to graduate from static spreadsheets to their own “passion projects” now available to them through enhanced analytical tools. Given such empowerment, the usual result is that—on their own—users now discover white spaces and data patterns that were hitherto unknown.

    But ease of access to relevant datasets is imperative for this to occur.

    Across multiple engagements, one of the lessons we learned is that while job function does matter, at times even the most junior employees are able to churn out Sankey diagrams that explain cash flows—while middle levels are satisfied with the bare minimum aggregate information.

    Both risks and opportunities are often hidden in the details. A powerful data visualization platform connects users, provides options for various user roles, and facilitates ease of communication with charts, graphs, and rollups.

    Read the Infocepts Data Storytelling Guide to learn more about how to communicate information tailored to an audience using compelling visuals and narratives.

  4. Elevate the surrounding systems and infrastructure – Data literacy projects yield the desired value when they’re not treated on a standalone basis. To drive change in their company culture, our clients have realized maximum benefits when adopting a long-range view. It includes data governance office oversight, self-service analytics team support, and a focus on both data quality and data discovery.

    While this might seem like a large intervention, within each department you could encourage users to identify the top three problems where they invest significant time in data collation or arguing about data veracity. Piloting such a use case helps propagate the desired messages, as well as establish the systems and behavioral tweaks required to sustain a larger program.

    Without the right enabling factors there is often dilution of goals. Poor data quality datasets dampen user interest, while a trusted data dictionary enables even new workforce entrants to find desired reports on built-in, Google-like search portals. This cuts down on manual dependence while also creating a self-sustaining cycle of interest. In turn that fosters more inquiries, a governed cadence of process changes, and emphasis on the best use of data.

    For one of the global retailers Infocepts initially implemented a data cataloguing solution that increased users’ understanding of data by 5X and boosted adoption. At the same time, it reduced IT helpdesk costs by more than half. The data literacy program with focus on education and engagement, provided an overarching layer on top of the data projects.

  5. Involve data scientists within the mainstream – The business environment has moved beyond historical and diagnostic analytics. Featuring the highest levels of automation, it now involves machine learning of data patterns to predict future scenarios and model the best responses based on user behavior. To retain competitive positioning, you must embrace data science approaches. Only in this way can you make a razor-sharp pitch to predict future defaults or the next best option to offer your customers.

    Gone are the days when e-commerce, point of sale (POS), and enterprise resource planning (ERP) teams operated in silos. With data being the connecting glue, data scientists can easily model customer behavior from the data stored in these systems to determine the right pricing and product positioning to fuel greater sales. Your workforce excels when it has deeper knowledge of—and access to—scenarios and customer data; all the more in a fast-moving environment.

    For one of Infocepts’ health informatics clients, we boosted its data science capability to aid new product development ideas—initially by developing various statistical models on its analytics platform. Tailored data science trainings for the staff nourished these product ideas and enhanced organization performance—yielding a 150% rise in new data science use cases.

  6. Adopt a continuous cycle of improvements – Managing the motivation of your workforce and engaging them in continuous cycles of innovation is critical in data literacy engagements. It also helps if the shift in performance metrics of your workforce is visible across cross-functional business teams, and key contributors are also recognized.

    While data literacy programs focus primarily on training, we must acknowledge how data communities and informal hackathons, or contests can crowd-source new ideas that lift morale and improve user adoption. You will benefit from a program that stitches together a continuous cycle of learning, reinforced with incentivization to make your organization data literate.

    Infocepts has helped a leading global market research firm boost user adoption by implementing a continuous improvement strategy. Apart from skill development, we worked closely with the client to bring a significant shift in speed of data delivery, establish a more data savvy workforce, and improve operational excellence.

Get started with Infocepts’ comprehensive data literacy program to help accelerate ROI from your data-driven investments.

References:

  1. https://www.statista.com/statistics/1186304/total-enterprise-data-volume-location/
  2. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
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One thing businesses learned from COVID-19 was to rapidly adapt to the changing situation in real-time. It’s more important than ever for businesses to have a truly data-driven culture versus having unplanned, isolated pockets of insights. It also becomes critical to equip frontline users with data-driven insights, thereby empowering such decision-makers with modern, self-service analytics to help your business grow.

Challenge with traditional analytics – Since the earliest days of BI reporting, IT teams and analysts curated data and reports for business users to consume. Self-service reporting became popular later, but it required days-long training and handholding to use visualization tools and for users to get used to newer platforms. In most cases, data was not catalogued. Or it was so poorly cataloged that it resulted in even more data and knowledge silos limited to certain sets of users only. Such dependency still lies with those technical teams charged with building reports for non-technical consumers.

Search-driven analytics for speed and scale – Search features are powerful; improves user experience; and you can find them inside most modern apps and websites. With Search-driven analytics, users are empowered to search for information in a much simpler way, engage with the data, and make faster business decisions (a long, complex process with the traditional analytics tools).

Four Reasons for Adopting Search-Driven Analytics – the Next Big Thing in BI

Improved user adoption with intelligent search – In a generic sense, search has transformed everyone’s lives—there is no training required to quickly access information anyone needs. For fast and easy access, users perform a simple Google search or ask Siri, et al.

As for search-driven business analytics, minimal training is required for users to independently get insights from retrieved data on the fly. Built-in, intelligent search eliminates data silos and provides users with faster results and insights—despite having bigger data workloads.

Democratized data across the enterprise – In the simplest terms, data democratization means everybody has access to the same information; there are no gatekeepers repeatedly creating bottlenecks. Puzzled by data-related questions, built-in search capabilities enable everyone to effortlessly seek and analyze data to 1) expedite decision-making and 2) identify opportunities for the enterprise.

Faster time to insights – Using yesterday’s models, anytime a question entered a business user’s mind (and with limited reports or dashboards not readily providing the answers), they had to contact multiple teams and stakeholders, getting tied up with data stewards, analysts, and IT teams. This tedious process takes a few days until an answer is finally delivered in the form of a new report.

But with search-driven analytics, the ability for users to take action far more quickly is greatly enhanced. Every user is equipped with data, and ease of using search-driven analytics establishes a true data-driven culture. Now armed with these faster insights, businesses can focus on critical growth opportunities, fine-tune operations, and make faster decisions for improving customer and user experiences.

Improved productivity and lower costs – Today, many enterprise data teams are spending significant time in collecting data to answer questions coming from users. Modern search-driven analytics eliminates this resource challenge.

One tangible benefit is lower operational cost (OPEX), lower per-user cost, and a significant reduction in the request queue for IT teams and others. Their improved productivity means they can cater to more critical business tasks and customer needs.

In addition, you eliminate business opportunity costs by empowering users to generate actionable insights by themselves. Discovering they’re able to quickly and easily answer day-to-day data questions on their own, this new-found ability enhances application adoption rates.

Bringing Modern Search Capabilities to All Businesses

Modern self-service is not just for data analysts—it’s for all users to engage directly with your data and get their questions answered quickly and easily. In an easy-to-use manner, search-driven analytics makes it possible for everyone to get the data they seek and draw insights by simply asking questions. By ‘talking to the data,’ they can use their own voice to instantly comb through sources and access insights—whenever and wherever they’re needed.

Leveraging AI, machine learning, natural language processing and natural language generation, we help our clients build augmented business and conversational apps with best-in-class accelerators. Talk to us to learn how—by leveraging search-driven tools or by integration with the likes of Alexa, Siri, and Google-like search interfaces—Infocepts can bring conversational capability into your enterprise analytics and platforms—making exploratory insights available to all users.

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In the digital age, organizations are constantly reimagining their customer experience (CX) strategy to improve business outcomes by discovering what really matters to their customers and finding new ways to improve customer engagements. During the pandemic, most businesses have transitioned from physical to digital and have adopted the new normal. Customer behavior and expectations have changed further due to this accelerated digitization and the new reality is here to stay. This shift to an automated digitization approach and changing customer expectations has led organizations to smartly innovate, transform and personalize digital experiences by quantifying and analyzing the digital touchpoints during the end-to-end customer journey. As per Gartner1, about 48% of the companies felt a need to increase their investments in digital initiatives to provide an enhanced digital experience.

In this blog, we will understand the components of digital experience, and the key factors that help organizations improve digital experience for their internal and external customers.

Analytical Digital Experience – Paving the Way for Next Generation CX:

Customer experience driven by cutting-edge technology innovations and key actionable insights with analytics provides a superior multichannel user experience and higher user adoption for organizations. Here are the 3 major components of Digital Experiences that enable organizations to enhance their customer experience by effectively adopting and implementing digital technologies in their D&A platforms.

Digital Experience Components​

  1. Data Storytelling for Accelerating Business Decisions Enable your stakeholders to take data driven decisions by combining the data from multiple customer touchpoints into measurable outcomes. Data storytelling helps businesses build stories using data, visual and narratives to provide actionable insights for business growth. The customized data storytelling approach can be implemented through various platforms such as self-service BI tools, analytical web, and mobile applications, etc.
  2. User Experience for Increasing User Adoption It is important to follow a user-centric design approach by empathizing with the user needs through user research and usability tests covering all major digital touchpoints. This helps to create personalized digital experiences and increases the value-to-user index. This highly personalized approach is beneficial in implementing the user-persuasion techniques leading to higher user adoption.
  3. Visual Design for Enhancing Brand Value With increased digital exposure, it is important to create unified brand-to-customer visual engagement across all touchpoints to support a similar language of communication. This can be achieved by improving aesthetic appeal through thoughtfully including visual elements for enhanced digital brand experience.

Realizing Business Value and ROI from Effective Digital Experiences

To increase the overall customer experience and improve customer satisfaction (CSAT), it is important to derive parameters which quantify digital experiences by measuring the user behavior and their interaction with various digital touchpoints, analyzing them, and extracting actionable insights to improve the user adoption. Numerous organizations are practicing and implementing various data-driven strategies which involves analyzing the impact of personalization, brand awareness, loyalty, channel flexibility, and improved sales/service conversations have on the overall business.

Our tailored approach helps businesses to quantify the experiences, analyze them and take decisions based on the reports ensuring higher user adoption of their D&A platforms. Based on our experience, we have identified the five factors that contribute to great digital experiences leading to higher adoption.

  1. Efficiency and Insights Accuracy With a plethora of data available to be displayed through a single platform, it becomes difficult and complex for the users to navigate through data and realize what is needed. Organizations need a framework to measure completeness and accuracy of insights delivered through the platform that enables a user to meet his/her business goals.
  2. Ease of Use It is common for the users to explore the platform on-the-go leading to an increased drop-off rate due to information inconsistency. It is important to bridge the gap between both initial orientation and deeper learning of the platform features. Simplifying user journey with navigation self-help features improves platform awareness, helping reduce training time and costs.
  3. Performance and Agility With evolving user needs, it is important to have an agile support team that continuously optimizes the performance, adds new metrics with speed and modernizes the platform with required upgrades and integrations to keep the application relevant. Effective response rates and perceived user value is still at the core of overall customer satisfaction.
  4. Memorability To ensure user retention and reducing the drop-off rate, it is important for organizations to build intuitive, easy, memorable, and interactive platforms. These interfaces need to be pleasant, interesting to use and should provide exceptional user experiences.
  5. Error-Free With modern technologies and increased user volatility, it has become a priority to reduce the number of errors and data inconsistencies across applications and platforms. Organizations need to how well the platform prevents errors and how it helps the user recover from any errors, thus leading to increased adoption.

Infocepts Digital Experience solutions use modern data-driven frameworks that enable organizations to quickly quantify and measure customer experience metrics for predicting and understanding customers. It helps improve the digital experiences across channels and uses actionable insights for transforming customer experience strategy.

Want to know more? Get in touch with our team to enhance your digital experiences.

References:

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Businesses today look to extract more value from their BI applications by focusing on a user-first approach to speed up user adoption. Planning for changes across the enterprise and ways to improve user experience is critical when it comes to the success of a new BI implementation. Every BI application is created with the partnership of users, where their detailed requirements are structured, artifacts are designed, and the required functionalities are converted to features. But with every incremental product feature rollout, user adoption never meets the target because users are not accustomed to change.

In such situations, it is important to reduce the gap between the BI objectives and the usage of the application in the post-rollout phase to enable continuous engagement and eventual effective BI landscape evolvement. Also, after any release, continuous assessment is needed to baseline adoption, the impact on the overall BI objectives and re-assess user awareness or design complexities.

Any BI application, website, or product gains popularity when it has minimum complexities, and it follows a simplistic design to help users achieve goals faster and with more efficiency. There is a reason successful organizations like Google and Apple follow a simplistic design approach. BI applications that need minimal user training have a better chance of success. One should also keep in mind that the userbase of such applications would be a wide range of people from across the organization with varied skills, knowledge and expertise, and the solution should resonate with all.

We have worked with several customers across industries in their data adoption and democratization journey. Based on our experience, here are the top things to keep in mind while designing BI programs to enable higher adoption.

  1. Drive standardization of data and insights: Invest in creating standardized reports, dashboards and define a data structure that helps organizations quickly reply to user questions, improve productivity and user experience. With the increasing popularity of BI tools and their usage across business teams, there are now many complex and elaborative visualizations available under each tool. The key here would be to start with simpler visualizations standardized by analysis type and use complex ones only where they are really needed. Something as simple as a bar graph or a pie chart can provide the right information and does not need much user training. A good example would be standardizing trend reports which give key metrics by analyzing week-over-week (WoW), month-over-month (MoM), and year-over-year (YoY) data using trend charts.
  2. Assess product requirements and acceptability: A sluggish application will never be accepted by users. In this era of big data, users must deal with the vast ocean of data to solve problems and leverage product innovations. Faster data retrieval helps improve performance, and hence organizations need to plan an assessment of the digital landscape and how it affects the product or application. Also, applications have different visual requirements and achieving all can be a challenge, hence it is important to have realistic expectations set with the users and all stakeholders. A good example of this is, if end users do not need the entire data, aggregated datasets should be provided. Certain data limits can also be put in place over a larger dataset to allow efficient retrieval for maximum use cases. This should be agreed upon upfront with the stakeholders.
  3. Enable self-service: With increasing user expectations, customization and personalization plays a vital role in setting any product or application apart from others. Although things like pre-canned reports would be the center stage of the application, it is also important to provide an easy play area for the users to create their own custom reports. This enables self-service for data requirements and creates opportunities for more enhancements as and when users want more analysis. This helps the users to explore existing parameters and new possibilities. Adding certain filters to the default template also helps users limit the extracted data and prevent queries against the entire dataset. Preparing datasets for ease of use and appropriate training is key for self-service adoption.
  4. Define data dictionary for seamless data management: Larger enterprises have many divisions and sub-organizations which in turn have different taxonomies and terminologies for the same data elements. It is important to have a data dictionary accessible from the application which would easily explain different elements used in any report. This saves a good amount of time and reduces basic questions about applications or any of the associated processes. Tools like MicroStrategy and Tableau provide features like attribute description and field description respectively which can be used to enter business definitions of every field. This is available for any dashboard, report, or self-service report which uses the same object thereby creating an in-sync environment.
  5. Evangelize application and train users: Internal marketing should be part of every BI application’s overall planning. Creating awareness across teams helps increase adoption, cross-functional learning and promotes internal systems. It is also equally important to plan and roll-out a well-defined training strategy by working closely with training and other relevant teams. Key user training material should be made easily available which helps users tackle frequent questions and queries. Ensure your training strategy includes the following:
    1. Training videos for how to use a report or dashboard
    2. Training gifs for features like how to change a filter, move or add columns
    3. User manuals available for quick download
    4. Help section for Q&A requests
    5. Help desk set-up for users to come in/log tickets to clarify queries if any
  6. Analyze usage and publish performance metrics: To gauge if insights delivered are really helping the users, it is important to have certain usage metrics tracked for analyzing performance. Analyzing valuable metrics which show usage and user analysis is critical to track adoption and its trends. These metrics help us analyze and improve the application by removing unwanted objects, starting proactive maintenance, and improving the overall health of any business application. Below are a few common examples of metrics we have implemented for our customers to track usage. These are typically monthly, and variance compared with previous months
    1. Users added every month
    2. Total new users
    3. Active and inactive users
    4. User activity analysis
    5. Time spent per activity/execution
    6. Total reports accessed
    7. Usage of collaboration features

In Summary, an ideal application that guarantees user adoption would be the one that requires minimal user training, is simplistic in design, and answers the maximum user queries with ease. Focusing on excelling in these areas as part of the product planning ensures return on investment for initiatives across BI landscape and the overall digital ecosystem. It reduces the overhead on business stakeholders like IT teams, Support, Finance, and others, who spend nearly 10% of their bandwidth in catering to basic questions coming from various users of such applications.

To know more, read our e-book: Improving User Adoption: Bridging the gap between data and users on how Infocepts can help you transform your BI application portfolio. Talk to us to know more.

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I was watching a movie “Ford Vs Ferrari” over this weekend which depicts one of the most epic rivalries in the world of Automobile. The biopic film shows the quest of a car designer and driver cum engineering specialist who wants to build a world-class racing car for Ford Motors which is capable enough to beat Ferrari at Le Mans, a 24-hour race. To make this happen, Carroll Shelby (car designer) sensitizes Henry Ford-II about multiple bureaucratic red tapes at Ford Motors that they need to leap through to seek reduction in car’s feedback loop.

This reminds me of “Conway’s Law” which when applied to enterprises using various software systems implies – “Organizations are constrained to produce system designs which reflect its own communication style.” Conway’s law provides a particularly important hint towards addressing challenges due to complex data teams and their data pipelines in data analytics systems.

This brings the need of “DataOps” to the fore!

Much more than hype

DataOps is a methodology to automate and optimize challenges in data management to deliver data through its lifecycle. It is based on the similar collaborative culture of Agile and DevOps foundations to balance control and quality with continuous delivery of data insights.

The landscape of data and business intelligence technologies are changing by leaps and bounds. As enterprises try to maximize value from data over a period, they moved from relational databases (RDBMS) to data warehouses (DW) to address growing data volume challenges, then from data warehouse (DW) to data lake (DL) enabled by cloud to address scalability and reliability challenges. Recently some teams have been migrating from data lake (DL) to Delta Lake for turning data lake transactional and to avoid reprocessing.

The evolving architecture patterns and the increasing complexity of all the data V’s (volume, variety, veracity etc.) is impacting the performance and agility of data pipelines. Businesses need more agile, on-demand, quality data to serve newer customer demands and keep innovating continuously to stay relevant in the industry.

Even though DataOps sounds like yet another marketing jargon in heavily crowded list of “*Ops” terms used within software industry, it has its own significance and importance. As stated in Conway’s law, different data teams scattered across organizations in the form of traditional roles (data architects, data analysts, data engineers etc.) as well as newer roles (machine learning (ML) engineers, data scientists, product owners etc.) work in silos. These data stakeholders need to come together to deliver data products and services in an agile, efficient, and collaborative manner.

DataOps addresses this concern along with bringing agility and reducing waste in time-to-value cycle through automation, governance, and monitoring processes. It also enables cross-functional analytics where enterprises can collaborate, replicate, and integrate analytics across their business value chain.

The method to madness!

The common goal of any enterprise data strategy is to utilize data assets effectively to fulfil an organization’s vision. DataOps plays a pivotal role in operationalizing this strategy through the data lifecycle. A set of steps to help you design a holistic DataOps solution design is outlined below:

Assess where you stand:

To design a DataOps solution that guarantees adoption, a detailed study involving enterprise people, process and technology is required. An enterprise-wide survey outlining current maturity through questionnaires is a great beginning to this journey. Undertake a maturity assessment involving key stakeholders within the enterprise covering the following areas:

  • Customer journeys and digital touchpoints
  • Enterprise data culture
  • DevOps lifecycle processes and tools
  • Infrastructure and application readiness
  • Orchestration platforms and monitoring frameworks
  • Skillset availability and roles definition
  • Culture and collaboration across teams and functions

Design for outcomes:

A well-designed DataOps solution should have the following capabilities. Ensure these capabilities are catered to in your DataOps solution design.

  • Real-Time Data Management – Single view of data, changes captured in real-time to make data available faster
  • Seamless Data Ingestion and Integration – Ingest data from any given source database, API, ERP, CRM etc.
  • End-to-End Orchestration and Automation – Orchestration of data pipeline and automated data workflow from environment creation, data ingestion, data pipelines, testing to notifications for stakeholders
  • 360-Degree Monitoring – Monitoring end-to-end data pipeline using techniques like SPC (statistical process control) to ensure quality code, data, and processes
  • Staging Environments and Continuous Testing – Customized Sandbox workspaces for development, testing to higher environments which promotes reuse
  • Elevated Security and Governance – Enabling self-service capability with a secure (metadata, storage, data access etc.) as well as governed (auth/permissions, audit, stewardship etc.) solution

Make the right tool choices:

Make tool choices based on your use case, enterprise goals for DataOps and the capabilities you have considered as part of your design. Some tool choice considerations are provided below.

  • DataOps solutions can be implemented using COTS (commercial off-the-shelf) tools or can be custom-built. To become a mature DataOps enterprise, it is important to have a repository of components that can be reused.
  • There are specialized COTS tools that provide DataOps capabilities only or provide a mix of data management and DataOps capabilities. Some examples of COTS DataOps tools include: DataKitchen, DataOps.live, Zaloni, Unravel and so on.
  • There are also several open source or cloud-native tool options that you could combine to implement your DataOps solution. Ex: GitHub, Jenkins, Nifi, Airflow, Spark, Ansible and so on.

In Summary, DataOps also allows enterprises to get better insights into pipeline operations, deliver data faster, bring resilience to handle changes and deliver better business results. DataOps enables organizations to take a step towards excellence in data transformation efforts and helps accelerate their IT modernization journey. It also empowers organizations to embrace change, drive business value through analytics and gain a competitive advantage in the market.

Get started with Infocepts to accelerate your DataOps strategy and implementation across the business value chain.

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Taking a few steps back into history, a lot of time, meaningful interactions, analysis, and creativity went into developing a pixel-perfect dashboard that answered business problems. Even though this can solve complex problems, with an ever-growing need to improve business efficiency at speed, organizations needed a way to handle time-critical problems. It made this a motivation for introducing Self-Service Analytics to let businesses take control of analysing the data by themselves and save time complexities.

In today’s digital age, disruptions in technology have helped organizations to take a step forward in their quest to be data-driven by adopting new ways of self-service analytics which uses data from human conversations or voice. Conversational Analytics has numerous BI use-case applications, and it uses natural language processing (NLP) and cognitive techniques to transform conversational data to insights in seconds.

Let us see how conversational apps are helping organization improve their analytics needs.

Personal Google Assistant for your Data

Revamping the approach of drag and drop in Self-Service BI to offer solutions where customer can use Google like search in their organisation’s data, conversational apps have come a long and interesting way. Conversational Apps interfaces are intelligent enough to understand the user data and offer suggestions making it easier for the user to execute their query. Users can easily apply filters or calculations on a search query. Just like Google lists the search results instantly irrespective of the volume of data it processes, these apps have been designed to handle vast gamut of data to give instant result.

Empower Users with Simple Intuitive Interface

Another and most important aspect of modernizing today’s BI is to empower as many users as possible since the analytics adoption rate still rests at just 30%. Even after advancements in self-service sector, 84% of frontline workers report poor analytics experiences and need better insights technology. It should be easier for every user to interact with data through natural language queries (NLQ), search, voice, helping them focus on the reasons and key drivers derived from the data. Providing the users with a simple but intuitive interface and not letting them worry about data complexities due to inbuilt and advanced natural language processing techniques, brings the much-needed change. This way conversational analytics can bridge the gap by targeting the business savvy users and pushing the analytics adoption to 50% thus contributing to better reach.

Visualizations Coupled with Natural Language Narratives

It is also important to understand and portray what your data knows. Whilst conversational applications are being equipped to know what data values are being looked for and then build the most meaningful story from the extract, it also proactively adds different perspectives by representing in the best possible visualization or letting the user choose one from the existing stack. One can also see an apt implementation of natural language generation (NLG) to narrate these perspectives. These apps are also developer-friendly to give more flexibility for the business users to present the story the way they want and where they want. Easy configuration options and availability of a range of APIs makes this possible and allows businesses to focus on deriving value from their data.

Personalized Insights

Even though text and voice-based search interfaces give conversational apps the edge over traditional Self-Service Analytics, what makes conversational apps more interesting is its ability to understand insights that are relevant to the users using cognitive techniques. This knowledge is then used to proactively discover similar analysis. Be it relevant insights across the department or organization, conversational apps bring them closer to you by recommending them on your homepage. This level of “Personalization” helps business gain insights into their preferences and intent through data. Shaping up your data so easily and giving a personalized touch makes Conversational Apps more appealing.

AI-Driven Deep Insights

Conversational apps leverage AI-ML right from pulling and understanding customer data or schema, to gathering insights from it. Be it diagnostic, prescriptive, or predictive analysis, these apps are rapidly evolving with every new version, giving more options for businesses to explore their data and take business decisions instantly. They offer integration solutions which uses the latest and trending data engineering tools that expand the scope of implementing complex and use-case-specific machine learning models on any given data. With these solutions at their fingertips, businesses can now envision how AI-ML can bring more meaning to their data and focus their AI investments in the right direction. Advancements in NLP and ML techniques combined with increasing maturity of conversational AI and RPA platforms can help businesses find deeper insights from large volumes of conversational data.

To summarize, in a BI landscape conversational apps have given more control of the BI-layer to business users. By building smarter personalized interfaces and bringing AI to the doorstep, conversational apps are expanding the horizons of Self-Service BI making it simpler for business to increase their adoption rate.

At Infocepts

Infocepts Conversational app solutions allow customers to adopt and embed conversational capabilities without having to worry about platform complexities and management. Infocepts’ proprietary accelerators built to work across platforms, leverage AI, ML, NLG to make exploratory insights quickly available to all users.

Be it integration of NLP capabilities in existing BI setup or creating and managing a new conversational apps platform like ThoughtSpot or Power BI Q&A, one can get started with modernizing their BI stack instantly and take the first step towards data-driven modernization.

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Natural Language Processing (NLP) helps computers understand informal human language as it is typed or spoken out. The latest innovations in NLP technology are revolutionising human-machine interactions. For decades, all data and analytics users have been looking for easier ways to interact with data and present insights in a simpler manner. As computers get better at understanding natural human language, analytics applications can leverage this capability to instantly connect decision makers with the right business data. Search and AI-driven analytics provides a google-like experience on top of data, thus making the use of analytics tools as easy as having a conversation with a virtual assistant or a modern search interface.

Gartner’s* data and analytics trends signifies the importance of Natural Language Processing (NLP) by including it amongst the top 10 data and analytics technology trends.

Below are the top six motivations for organizations to adopt Search and AI-driven Analytics along with traditional Business Intelligence (BI) dashboards and reports. These motivations are based on experiences from multiple customer success stories across industries and observations from numerous use cases at various stages of data maturity.

  1. Improve data and analytics adoption: As global organizations focus their efforts on democratizing analytics for everyone, it is observed that most of the data initiatives are unsuccessful due to low analytics adoption and usage rates. High complexity of analytics tools and platforms used by most business teams is the primary factor for organizations failing in their efforts to be data driven. To overcome this, organizations are required to spend significant efforts on training and change management to enable users with the right skills.NLP features offer simplicity of use and significantly reduces or eliminates training efforts. With the increasing use on NLP, most of the analytics queries will be generated by NLP search/voice or will be automatically generated by the analytics tool. This boost’s analytics and BI adoption from 35% to over 50% for all employees and business users. It also enables businesses to deliver analytics anywhere and to everyone in the organization, with less needed skills, and reduced interpretation bias than current manual processes.
  2. Reduce time to actionable insights: Swift decision-making in uncertain business scenario is another challenge where NLP techniques are helping in providing automated vital insights and predictions on-the-fly. Businesses can be better equipped to predict, prepare, and respond in a proactive accelerated manner.
  3. Extract value from untapped data sources: Around 80% of enterprise information is either unstructured or ignored by business teams. NLP combined with AI helps to derive value out of untapped and unstructured information assets like text, voice, video and images for enhanced insight discovery, reduced costs, and inefficiencies inherent to manual data collection and data entry.
  4. Enable frontline users with embedded operational insights: NLP provides users with dynamic data stories with more automated, intelligent, and customized experiences, compared to dashboards which provide point-and-click authoring and exploration. Implementation of NLP allows streaming of in-context ad-hoc analysis which provides the most relevant insights to each user based on their context, role, or use.
  5. Empower executive users with innovative features: Due to time constraints, business leaders and executives normally do not get the opportunity to deep-dive into dashboards. NLP allows users to analyze data and provide important summaries which can be delivered in the form of a newsletter, or a short voice note (quick narrative). Conversational features can also help to answer further questions by the users and provide a 360-degree view on request.
  6. Rationalize BI workload: There is a constant increase in total cost of ownership for running and maintaining Data and Analytics systems. With adoption of NLP, typically 50-60% of current BI workload (comprising of static reports and canned dashboards) can be consolidated and redirected to NLP features and voice guided applications. This can help significantly reduce several operational, infrastructure and staff costs, and accelerate democratized analysis of complex data.

Are you wondering what NLP use cases can add value to your organization?

Infocepts specializes in identifying the right use cases for search-driven analytics and implementing them using industry leading tools.

Get in touch to know more!

References

* Gartner Article: ‘Gartner Top 10 Trends in Data and Analytics for 2020’, by Laurence Goasduff, 19 October 2020 – https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020

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Organizations are becoming more data-driven to take business decisions faster than ever before. Business users expect quick interpretations of their data to act on insights in real-time. This is where data storytelling kicks in. Data alone, can’t tell you insights, but through data storytelling techniques you can better understand the data and get faster and easier access to key business insights. Data storytelling is about creating a compelling narrative anchored by three basic elements: Data, Visuals, and Narratives. Data-driven storytelling often involves the use of visualization techniques to support or complement a written or spoken narrative. In this blog, we will dig into why narratives in data storytelling are so important.

What are Narratives in Data Stories?

Effective storytelling with data moves the audience to understand the core of your insights, understand the key takeaways, and at the end act on your recommendation. Story narratives are nothing but written messages or annotation of the key data insights. Story narratives play an important role in convincing your audience to understand the insights and persuade them to act on them. Mostly narrative acts as a supporting element to visual stories, which goes well with other data visualization. Narratives can be static, dynamic, or automated depending on what type of data story you are telling your audience.

Static Narratives:

Static narratives are primarily used to convey key insights along with visualizations using simple, everyday human language. A static narrative approach is used to tell explanatory stories to your audience. You can get your point of view across clearly to your audience using simple narratives along with effective visualizations. The static narrative follows a linear approach in which data is presented in chronological order using a traditional story arc with beginning, middle, and end. It focuses on heavy messaging and clarity of thoughts and a specific call to action from the audience’s perspective. Typically, you will find such a narrative approach followed in author-driven data stories such as data journalism, PowerPoint decks, infographics, and static representations of data. Though this is the most common and standard approach of telling narrative stories at times it might give biased perspectives because of the inability to deep dive into data.

Let’s see a static narrative example:

Dynamic Narratives:

Dynamic narratives can be a good alternative option in absence of automated narratives to create basic narratives to support your visual story. These focus more on the explanatory analysis by highlighting key KPIs and other important data insights using pre-defined dimensions and measures. This works as an out-of-the-box feature of an analytical tool to summarizes visualizations or report narratives that are customizable. These narratives are not auto generated so they often require more manual effort to craft sentences using static and dynamic text fields. You can map the text to existing fields, dimensions, and measures and get the values driven through charts or filters. You also have all basic formatting options available to highlight the key insights which require more attention from the end-user perspective. This option is good for telling short dynamic narrative stories in absence of automated Natural Language Generation (NLG) driven narratives.

Here is a dynamic narratives example:

Automated Narratives:

Next-gen analytical tools highlight automated narrative features in their analytical apps to quickly summarize visuals and reports by providing built-in customizable insights. These features are highly dynamic and with one click they automatically generate a summary of your key insights from your visualization. It combines the art of data storytelling with Natural Language Generation (NLG) capabilities. NLG capabilities interpret the data and generate English commentary (narrative) on the underlying data as a result, instead of trying to derive patterns or relationships from standalone visualizations, users can act on NLG-powered recommendations. Such a machine learning-powered narrative approach allows the end-user to interact with data, ask questions, explore, and tell their own data story. It is good for explanatory and exploratory data analysis.

Here is an example of automated narratives:

Overall narrative plays an important role in laying a solid foundation for making data-driven decisions by giving a glimpse of the data story in a more structured manner to display the hidden truth behind the data.

What to learn more? Get Started with our Data Storytelling solution to accelerate your data-driven decisions.

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You get home after a bike ride and check out your average speed, peak heart rate, VO2 max on your Garmin. From here you decide whether you had a “good ride”, or how you can improve next time.

Or, you’re looking into your stocks, and a notification pops-up on your smartphone. You open your trading app, read the news article, study a chart, and then make a trade within the same app.

“What’s common between these two acts?”

As a user, while strategizing about your next ride or making a trade, you are intuitively (almost subconsciously) making data-driven decisions while engrossed in your area of interest. The fact that you are consuming and analyzing data fades away in the background while you focus on the task at hand.

“So why isn’t this the case when I have to make business decisions?” It’s a valid question, and a short answer to it is, embedded analytics.

What is embedded analytics?

Simply put, embedded analytics is the seamless integration of analytic content and capabilities within applications (like CRM, ERP) or portals (intranets or extranets).

According to Gartner, “Embedded analytics is a digital workplace capability where data analysis occurs within a user’s natural workflow, without the need to toggle to another application”

The goal of embedded analytics is to help users make better decisions by incorporating relevant data and analytics to solve high-value business problems and work more efficiently as these capabilities are available inside the applications they use every day. This contrasts with traditional business intelligence tools/platforms, which focus on extracting insights from data within the silo of analysis.

How to get started with embedded analytics

“What are the key considerations for embarking on an embedded analytics journey for my organization?”, “How can Infocepts help?” you may ask. Curious you! But don’t worry, you are asking the right questions. Let’s begin with the second question first.

Infocepts is a global leader of end-to-end data & analytics solutions with nearly 20 years of experience enabling customers to derive value from a variety of data-driven capabilities. Working in partnership with you, we offer reusable solutions to your data and analytic needs to deliver predictable outcomes with guaranteed ROI.

Back to the first question on key considerations – blending our experience, expertise, and a lot of thought, we have put together a list of key considerations in case you are considering about creation and sustenance of embedded analytics applications:

1. User experience (UX) and adoption

The end-users should be able to use and adopt the analytics applications, else all the time and resources spent may not lead to anything.

2. Business Value

How to capture and articulate business value to secure “buy-in” from program sponsor(s) and key stakeholders. This involves creating a business case, identifying metrics to track, and measuring performance over a period of time.

3. Ability to customize, integrate and maintain

These factors determine the effectiveness of the application and ultimately determine whether its adoption is sustainable or not.

4. Governed self-service

Users should be self-reliant to use these apps. Moreover, it’s crucial that they have confidence in the quality of the data and the right person should be able to get access to the right information.

5. Operational efficiency and automation

The analytics application should help the users to do things faster, with continuous improvement.

How we created an embedded analytics solution

We helped a well-known luxury retail customer create an embedded analytics solution within their portal, which eventually helped them realize business value.

The customer had a unique business model wherein travel partners would bring in customers to their stores situated in the choicest of travel destinations around the world as a part of their tour itinerary. These travel partners would log in to a portal provided by our customer and make registrations for the tour groups so that the store operations team could plan their visits accordingly and provide a better customer experience for the tour group within the store.

After analytics were seamlessly embedded within the login portal, travel partners were empowered to get access to key information about their sales, targets, and commissions/incentives in a single place. Here’s how it helped both parties:

Travel partners:

    • Better visibility and actionable insights based on past sales performance trends by customer demographics and product categories helped them plan and hence perform better. For example, they understood from data that people coming from Switzerland to France store may not be too keen to buy watches since they may have already bought those in their initial part of the tour itinerary. Such insights helped the travel partners to plan out, in collaboration with our customer, some tailored store visits thus maximizing sales performance.
    • Data-driven insights also empowered travel partner associates to fast-track their effectiveness, which would otherwise typically take years of experience to understand patterns.
    • Since data points were ‘embedded’ within the same portal which the partners were quite used to working with, the familiar interface meant minimal change management and training, and hence almost 100% adoption.

Our customer:

Retail stores saw a 30% uptick in channel sales due to embedded analytics. Moreover, it reduced the workload on the store operations team in managing the channel partners and their information requests. Instead of pulling one-off reports time and again, they were able to focus their time on other activities to better the business.

 

Want to learn more? Get started today with your embedded analytics journey and help build a better future for your business and users.

 

Further, Infocepts was featured as the Top 15 Business Analytics Blogs by Feedspot

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There is a general belief that narrating stories or data storytelling is a specialized skilled job that must be done by data visualizers or data storytellers using sophisticated design and BI tools. However, it is imperative these days that everyone becomes a data storyteller in some capacity. With increasing data volumes and heightened interactions with data daily, having visual communication skills, is a powerful skill to have.

So, how do you do this? Whether it is a client presentation, pitching clients, quarterly business reviews, or internal team discussions, opportunities are endless to become a Data Storyteller. You might even be doing it already and not even know it!

There are five key focus areas to master becoming an effective Data Storyteller.

1. Visual thinking

Usually people often tend to present facts and figures in their absolute form such as charts and tables, without any storyline or narration around it. For the audience, it is difficult to consume or remember trends and patterns in the charts. Here is where Data Storytelling comes into play.

Take your common spreadsheet full of data highlighting your company’s quarterly results. This might be presented to the audience in a formal setting and the purpose is declarative. Instead, if you were to utilize the art of Data Storytelling, you start to think visually, considering the nature and purpose of the visualizations. Before presenting, ask yourself these two questions:

Is the information conceptual or data-driven? Here the goal is to present the idea.
Am I declaring something or exploring something? Here the goal is to inform and enlighten.

2. Which hat do you wear when you see the data?

Narrating a story from the data can often be complex and time consuming if not done right. Beginning with data in its raw stage to the final output of presentation; a data set gets processed and passed upon through multiple profiles. When working with data sets there are four hats from a domain expert to an analyst, to a statistician, and finally a designer that you should wear.

3. Sketch an idea from the data

You don’t need to be a graphic design expert, using something as basic as pen and paper you can easily sketch out what you want to get across as a first step. Sketching out the data can help integrate various kinds of knowledge at the ideation stage. De-cluttering a heavy data set by sketching helps to identify relevant information, its hierarchy, and flow.

Sketching relies on conceptual metaphors, taking place in more-informal settings, such as off-sites, strategy sessions, or early-phase innovation projects. It can be used to find new ways of seeing how the business works or to answer complex managerial challenge such as restructuring an organization, producing a new business process, or codifying a system for decision making.

4. Power of metaphors in data storytelling

Have you ever visualized an area chart that resembles a mountain? As a metaphor, mountain can be used as an element of expression on how the company sales performance grew over the years touching its peak.

Numerous metaphors are woven into the fabric of data science itself, such as data warehouses, data lakes, and data mining. When narrating stories, metaphors are essential for breaking down language barriers that stand between you and your audience. They work best when you have complex concepts or ideas to convey. Advantages of using metaphors are they:

  1. Make the story more interesting and fun
  2. Keep the audience engaged
  3. Help bring the meaning and significance of the data to the forefront
  4. Assimilate the unfamiliar by comparing it to what is familiar

5. Getting personal with data story telling

More often than not, more time is spent gathering the data than actually composing the story around it. Without a good data story, visualizations are relatively ineffective. The story connects the dots between the data and the audience. It reveals the data’s meaning and significance, educates, and transforms the audience.

Based on Freytag’s pyramid theory, every story has a beginning, a middle, and an end. Each of them has a different purpose. When structuring your story, be sure that each part achieves its purpose.

Moral of the data story

The data story you tell an audience requires more than just data and visualizations. Good data is only one essential element. A good data story is one that engages, entertains, educates, and transforms the audience to act upon it.

Ready to get started? No matter where you are in your data storytelling journey, we are here to help. Get started now!

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