As seen on Forbes.

Covid-19 accelerated digital transformation for enterprises in all industries — and today, cloud initiatives are at the core of digital transformation. Enterprises are likely to experience mixed success with their cloud initiatives in the medium term as they manage challenges such as severe skill shortages, evolving data architecture practices and myriad cloud vendors with similar offerings.

Drawing from our company’s experience of implementing more than 50 cloud initiatives across industries, here are my top five recommendations for business and IT leaders planning significant data and analytics (D&A) initiatives in the cloud.

1. Not everything that can be moved should be moved to the cloud.

Cloud migrations involve significant capital expenditures (CAPEX). In my experience, when you migrate old data applications to the cloud, you should not expect to see operating expenses (OPEX) savings for up to three years. In many cases, all layers are not migrated together due to interdependencies on other systems, leading to a hybrid approach with a combination of on-premises, private and public cloud hosting.

Carefully evaluate the suitability and need to migrate the following category of applications to the cloud:

  • End-of-life legacy applications or tool platforms
  • Applications built on comparable SaaS (Software as a Service) tools
  • Applications accessing data that requires stringent data security and privacy
  • Specific hardware-dependent applications

2. Plan to embrace a multi-cloud future.

Three leading public cloud players — Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) are adding capabilities, services and geographical locations at rapid pace. Most of their comparable services match each other in terms of cost and performance, and with no consolidation in sight, you can benefit from their competition.

Each of these cloud vendors do provide a few differentiating services. To enable the creation of cutting-edge data and analytics solutions, aim to leverage the best services available, regardless of which vendor provides it. For example, one of our clients — a leading media and entertainment company — uses a multi-cloud setup with AWS infrastructure and select AWS services for its data apps, Azure for email services and cloud native PaaS platforms like Domo and Snowflake for analytics.

Within your organization:

  • Discourage investment in single cloud vendor
  • Promote a culture of looking for the best services, comparing capabilities and costs across cloud vendors
  • Encourage technical teams to design data architectures that seamlessly use cross-cloud capabilities

3. Don’t let security be an afterthought.

According to the Verizon Data Breach Investigations Report (DBIR), most cybersecurity incidents now involve cloud infrastructure. We can expect the threat of data breaches to grow in the foreseeable future, and the responsibility for increasing security protections lies with enterprises.

In our work, we have seen that most cloud initiatives, especially enterprises’ early endeavors, try to address security requirements through native services. However, due to inadequate design, these solutions fall short of addressing all risks. Thankfully, there are a number of solutions from third-party vendors available that you can use to address this critical gap. Use these tools to:

  • Carefully assess security requirements
  • Invest early in holistic security solutions
  • Conduct frequent vulnerability scans

A global bank that we work with has implemented a unified data-centric security model with sensitive data-flow discovery, real-time monitoring, behavior analytics and protection across all operational and analytical applications (both on-premises and on-cloud).

4. Monitor all D&A solutions through a unified platform.

Given the nature of cloud services, any data and analytics platform migrated to the cloud gets decomposed into many independent solutions. While this offers advantages, such as no single point of failure and scalable performance, managing multiple platforms can be complex. In case of service level failures, it can be difficult to ascertain the root cause, replay the sequence of events and recover from the failure.

DevOps staff supporting disparate platforms need to invest significant effort in scanning consoles of multiple services for any meaningful analysis — post mortem or change impact. It is highly likely that components of such systems will drift away from the initial architectural vision. To avoid this outcome, push for:

  • Holistic assessment of current and future monitoring requirements
  • Early investment in a comprehensive monitoring solution
  • Frequent “game day” drills to test responses, in processes and people

A global market research firm we work with uses a centralized monitoring platform to track its infrastructure, databases, analytical apps, workflows and security. It gives them the ability to have a 360-degree, single-pane view of its data and analytics ecosystem and provides greater operational efficiency.

5. Aim for an accelerated pace of innovation through the cloud.

For most enterprises, the first set of cloud initiatives includes migrating existing data and analytics applications to a cloud platform. Whether as-is (lift and shift) or re-engineered, these types of migrations don’t change the status quo dramatically.

But there is a constantly expanding set of cloud offerings that covers capabilities like IoT, blockchain, data science, machine learning, media, quantum, robotics, satellite, VR and AR. Explore how your organization can use cloud initiatives to power innovation. How effectively you do this will prove to be a competitive advantage in the Industry 4.0 era.

There are also countless focused solutions available on cloud marketplaces that significantly reduce the cost of experimentation. Take advantage of these cost-effective tools and encourage:

  • A culture of innovation with cloud at the center
  • A risk appetite based on leveraging cloud offerings and marketplace solutions
  • Thinking “cloud first” before costly in-house development of new solutions

Your organization has probably moved the first set of data stores and front end analytics apps to the cloud with varying degrees of success. Enterprises that don’t see measurable positive outcomes with their first cloud initiatives tend to delay the rest of their cloud adoption journey. Don’t fall into the same trap. Cloud initiatives will continue to be a critical ingredient for future business capabilities. By finding the right solutions and engaging the right partners, you can set your organization up to make well-informed choices, develop pragmatic roadmaps and avoid the pitfalls that lead to failure.

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Covid-19 accelerated digital transformation for enterprises in all industries — and today, cloud initiatives are at the core of digital transformation. Enterprises are likely to experience mixed success with their cloud initiatives in the medium term as they manage challenges such as severe skill shortages, evolving data architecture practices and myriad cloud vendors with similar offerings.

Drawing from our company’s experience of implementing more than 50 cloud initiatives across industries, here are my top five recommendations for business and IT leaders planning significant data and analytics (D&A) initiatives in the cloud.

1. Not everything that can be moved should be moved to the cloud.

Cloud migrations involve significant capital expenditures (CAPEX). In my experience, when you migrate old data applications to the cloud, you should not expect to see operating expenses (OPEX) savings for up to three years. In many cases, all layers are not migrated together due to interdependencies on other systems, leading to a hybrid approach with a combination of on-premises, private and public cloud hosting.

Carefully evaluate the suitability and need to migrate the following category of applications to the cloud:

  • End-of-life legacy applications or tool platforms
  • Applications built on comparable SaaS (Software as a Service) tools
  • Applications accessing data that requires stringent data security and privacy
  • Specific hardware-dependent applications

2. Plan to embrace a multi-cloud future.

Three leading public cloud players — Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) are adding capabilities, services and geographical locations at rapid pace. Most of their comparable services match each other in terms of cost and performance, and with no consolidation in sight, you can benefit from their competition.

Each of these cloud vendors do provide a few differentiating services. To enable the creation of cutting-edge data and analytics solutions, aim to leverage the best services available, regardless of which vendor provides it. For example, one of our clients — a leading media and entertainment company — uses a multi-cloud setup with AWS infrastructure and select AWS services for its data apps, Azure for email services and cloud native PaaS platforms like Domo and Snowflake for analytics.

Within your organization:

  • Discourage investment in single cloud vendor
  • Promote a culture of looking for the best services, comparing capabilities and costs across cloud vendors
  • Encourage technical teams to design data architectures that seamlessly use cross-cloud capabilities

3. Don’t let security be an afterthought.

According to the Verizon Data Breach Investigations Report (DBIR), most cybersecurity incidents now involve cloud infrastructure. We can expect the threat of data breaches to grow in the foreseeable future, and the responsibility for increasing security protections lies with enterprises.

In our work, we have seen that most cloud initiatives, especially enterprises’ early endeavors, try to address security requirements through native services. However, due to inadequate design, these solutions fall short of addressing all risks. Thankfully, there are a number of solutions from third-party vendors available that you can use to address this critical gap. Use these tools to:

  • Carefully assess security requirements
  • Invest early in holistic security solutions
  • Conduct frequent vulnerability scans

A global bank that we work with has implemented a unified data-centric security model with sensitive data-flow discovery, real-time monitoring, behavior analytics and protection across all operational and analytical applications (both on-premises and on-cloud).

4. Monitor all D&A solutions through a unified platform.

Given the nature of cloud services, any data and analytics platform migrated to the cloud gets decomposed into many independent solutions. While this offers advantages, such as no single point of failure and scalable performance, managing multiple platforms can be complex. In case of service level failures, it can be difficult to ascertain the root cause, replay the sequence of events and recover from the failure.

DevOps staff supporting disparate platforms need to invest significant effort in scanning consoles of multiple services for any meaningful analysis — post mortem or change impact. It is highly likely that components of such systems will drift away from the initial architectural vision. To avoid this outcome, push for:

  • Holistic assessment of current and future monitoring requirements
  • Early investment in a comprehensive monitoring solution
  • Frequent “game day” drills to test responses, in processes and people

A global market research firm we work with uses a centralized monitoring platform to track its infrastructure, databases, analytical apps, workflows and security. It gives them the ability to have a 360-degree, single-pane view of its data and analytics ecosystem and provides greater operational efficiency.

5. Aim for an accelerated pace of innovation through the cloud.

For most enterprises, the first set of cloud initiatives includes migrating existing data and analytics applications to a cloud platform. Whether as-is (lift and shift) or re-engineered, these types of migrations don’t change the status quo dramatically.

But there is a constantly expanding set of cloud offerings that covers capabilities like IoT, blockchain, data science, machine learning, media, quantum, robotics, satellite, VR and AR. Explore how your organization can use cloud initiatives to power innovation. How effectively you do this will prove to be a competitive advantage in the Industry 4.0 era.

There are also countless focused solutions available on cloud marketplaces that significantly reduce the cost of experimentation. Take advantage of these cost-effective tools and encourage:

  • A culture of innovation with cloud at the center
  • A risk appetite based on leveraging cloud offerings and marketplace solutions
  • Thinking “cloud first” before costly in-house development of new solutions

Your organization has probably moved the first set of data stores and front end analytics apps to the cloud with varying degrees of success. Enterprises that don’t see measurable positive outcomes with their first cloud initiatives tend to delay the rest of their cloud adoption journey. Don’t fall into the same trap. Cloud initiatives will continue to be a critical ingredient for future business capabilities. By finding the right solutions and engaging the right partners, you can set your organization up to make well-informed choices, develop pragmatic roadmaps and avoid the pitfalls that lead to failure.

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Companies are seeing the promise of what cloud, data, analytics, and AI can do to transform their business, but many are struggling to realize the impact. Amongst the key challenges holding leaders back is the ability to access talent to fulfill their strategies. D&A talent – from solution architects to data engineers to business consultants – is hard to find and harder to retain. Meanwhile your executives are expecting results promised by your business case.

According to Forrester’s Business Technographics Data and Analytics Survey 2020,

“Nearly half (49%) of organizations are still at the beginner stage of their insights-driven business journey and only 60% of them have dedicated data, technical and insights staff available to teams across the organization to help them transform outcomes with data and insights”

Getting business results under these circumstances requires leaders to embrace new models and thinking for building D&A teams. In this webinar video, Our CEO, Shashank Garg and guest speaker, Forrester Research VP and Principal Analyst, Boris Evelson discuss tried and tested talent strategies to achieve success with D&A initiatives. Watch the video to understand:

  • Top challenges customers face when forming D&A teams
  • Strategies to deal with talent acquisition, development, and retention
  • Recommendations for strengthening your D&A talent strategy

Interesting Quotes from the Webinar

On Data Driven Businesses

“It was a few years ago when analysts such as myself and other economists predicted that insights driven businesses were going to grow 8 to 10 times, that’s not 8 or 10 percent faster, 8 to 10 times faster than the global economy, than industry averages, than competition. And today at Forrester, we absolutely are seeing this coming to fruition.”

On Central IT Teams

“If you fast-forward a few years, we know that central teams will struggle on the user engagement side and they will never be able to react to fast changes that are required to run modern businesses. You often see a huge sort of shadow IT coming up in the business unit. So you got central IT, you got shadow IT and then you know, they are doing anything and everything right from data wrangling to data ingestion to analytics. Not ideal!”

On AI and the Enterprise

“Artificial intelligence obviously is all over the place, we do not talk about any enterprise technology where AI is not taking a foothold. And while AI is helping us and helping enterprises get richer insights, deeper insights and further helping us democratize insights. AI requires some additional TLC, tender loving care in terms of building models, training the models and doing all of the model operations. So, yes, AI brings additional benefits but also requires additional care.”

On Competency Based Hiring

“We are saying that we don’t need a Snowflake or a Tableau developer, we need to hire for higher level competencies. So we need a data engineer, a cloud engineer, an analytics and data management professional who can go through these platforms and if I decide to switch, I don’t have to switch people, switching people is expensive. It should be easy to switch technology and cross-skill these people because they are embedded in that business, they understand my processes and they can make those shifts very very quickly.”

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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A Machine Learning and Operations (MLOps) platform plays a critical role in enabling data scientists to develop and train models to fulfill business priorities. The key asks business leaders have from their data science teams are centered on driving better sales and customer engagement. The MLOps platform provides the environment data scientists need to create and train models that achieve specific business objectives. Platform developers have a wide variety of artificial intelligence (AI) and machine learning (ML) technologies at their disposal. While such a platform lays the foundation for building data science models that can give businesses a disruptive advantage, developers have to comprehend with a problem of plenty.

How to Compose a Harmonious Tool Stack?

Getting out of the abundance syndrome requires developers to tap into their reverse engineering skills. They have to take stock of the key challenges faced by the business to shortlist technologies that can overcome them. Putting the right tool stack together then becomes a matter of matching must-have MLOps platform features with business priorities.

Characteristics of a Robust MLOps Platform

We have listed below few of the many characteristics of a robust MLOps platform.

  1. Scalability
    With a number of data scientists and ML engineers tinkering with multiple models at any given point of time, platform scalability is essential. If the platform is unable to support multiple users, their collective efforts to improve ML algorithms and codes will create a drag and reduce team productivity.
  2. Version Control
    The iterative nature of the tasks performed by data scientists requires them to test multiple models, optimize parameters, and tune features while dealing with a vast amount of data. Data science teams can’t be efficient if they cannot track model versions with changes to parameters, code, and data. Version control frameworks and Git repositories provide the means for tracking model versions and their performance.
  3. Data and Concept Drift Sensitivity
    With the passage of time, data and concept drifts become inevitable leading to inaccurate results. Models can be trained to trigger training and retraining routines when they detect drift patterns in data.
  4. A/B Testing
    The ability to conduct A/B testing is the cornerstone of developing effective data science models. An ideal MLOps platform should enable data science teams to put their models to test with different sets of users. It enables them to deploy models that are either at par with existing models or better than them.

Build an MLOps Platform Your Data Science Team Deserves

In this advisory note – we have defined effective strategies to build a robust and scalable MLOps platform.

Read Now

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This article was first published on Forbes.com, read the article here.

In the digital economy, data is our most valuable resource. “Data is the new oil” is now a popular saying, and it’s an apt metaphor. Companies that learn to extract and use data efficiently have the potential for huge rewards, driving profits, innovation and customer satisfaction.

Just like with oil, quality matters in data. Quality isn’t free; it requires time, money and attention, but it’s worth the investment.

What Is Good Data? 

Good data quality means the data is fit for use based on the business context and requirements. The business rules governing quality include both quantitative (e.g., “Is the date legitimate?”) and qualitative rules (e.g., “Is the date captured in the American notation?”). In addition, expectations of users regarding availability, reliability, usability, relevance and clarity may lead to perceptions of quality issues. Data quality initiatives need to address various aspects to ensure that data is trustworthy.

Think about global data related to Covid-19 vaccinations. Reliable data must include a patient’s date of birth, the date of each dose, type of vaccine, number of required doses and location of each dose. Quality issues become complicated when you consider that some users received shots from different vaccine manufacturers, or data may have been captured in different states or countries. Poor data quality can prevent various stakeholders — including public health experts, vaccine advocates, and the general public — from making informed decisions. If people perceive vaccine data to be unreliable, they may become more hesitant to get vaccinated and ultimately damage public health outcomes.

The Cost Of Bad Data 

In 2016, an IBM* study estimated that bad data costs the U.S. economy $3.1 trillion a year, and in 2020, a Gartner** survey found that organizations calculated that the average cost of poor data quality was $12.8 million a year.

In my experience leading a global data and analytics (D&A) solutions provider, I’ve seen that while everyone cares about data quality in theory, when it comes to actually making funding decisions, many customers want to cut corners.

But this is where the rubber meets the road. If you don’t finance data quality initiatives, then you won’t get the result you want. Poor quality data can lead to flawed decision making, top- and bottom-line consequences and decreased employee and customer satisfaction. Incomplete data may result in ineffective marketing campaigns, and a data breach can cause reputational damage or leave you vulnerable to litigation under laws like GDPR or CCPA.

Six Common Challenges In Data Quality Improvements

Improving data quality in your company will have significant long-term benefits, but you must be proactive. Here are six of the most common challenges to be aware of when improving data quality:

1. Lack of awareness: Because data is an intangible asset, it’s often hard to assess quality and address problems. Your stakeholders may not fully appreciate the state of data quality in your systems until a major issue impacts your business.

2. Difficulty justifying investments: To get buy-in on improving data quality, you need to be able to make a solid business case for it, showing how poor-quality data has had negative consequences in the past. But frontline staff may not be willing to document quality issues to build a future business case for something like automation, preferring instead to resolve issues manually.

3. Confusing shared responsibility with IT responsibility: Enterprise data is often used across multiple business units, moving through line-of-business systems into reporting and analysis systems. Quality ownership is delegated to IT as data flows through various pipelines, yet IT is not fully responsible for the source systems. Data quality demands shared responsibility.

4. Resistance to change: Data quality programs are heavily focused on continuous improvement, which calls for users in your organization to adopt new behaviors and perform additional checks and balances with discipline. If employees are unwilling to adapt, you will run into obstacles.

5. Fear of transparency: Data quality assessments and real-time quality dashboards may make some leaders uncomfortable. Looking into past data decisions and results may cause some in your organization to feel embarrassed or concerned, creating yet another roadblock.

6. Lack of sponsorship: Data quality initiatives often compete with new technology and product management investments. It can be tempting to throw money at a shiny new object, spending $X on a cloud computing platform instead of the same amount for a data governance consultant’s annual fee. Data quality is less glamorous and often loses out to modernization initiatives.

Five Ways To Improve Data Quality 

Once you’ve identified the challenges you’re facing, here are five actions you can take to address them:

1. Sponsor beyond budgets: To achieve successful data quality initiatives, you must be willing to invest more than dollars. Articulate the importance of data quality for the organization, inspire cross-functional collaboration, prioritize progress and hold frontline managers accountable for long-term success.

2. Invest in data quality and transparency tools: As the volume, variety and velocity of data increases, you need more specialized tools for quality management. Invest in software that automates quality management for profiling, catalogs, metadata and lineage.

3. Adopt DataOps automation practices: DataOps practices build on DevOps practices from the agile software engineering domain within data analytics. Integrate them to promote collaboration, communication and automation to bring business, applications, operations and data teams together to speed feedback cycles for quality issues and reduce cycle times for delivering D&A products.

4. Embrace a stewardship culture: To create a data-driven culture, you need to incorporate stewardship in your organizational norms and habits. This is a shared responsibility among various stakeholders and includes identifying and articulating quality issues and following governance practices to resolve issues.

5. Build a data management team: Data quality management requires dedicated skills, roles and positions depending on the scope, size and complexity of your organization’s data. Hire the people who can evaluate, design and execute the solutions you need, including Chief Data Officers, data analysts, business and technical stewards, tool experts and data management consultants.

Investing in data quality is a long but worthwhile journey. Evaluate your company’s needs and challenges, and invest in the practices, tools and leadership required to advance valuable data quality initiatives.

References:

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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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