Why KPISOFT wrote this

Where do you start?

The 5 stage Analytics-Based Digital Transformation

The THREE mistakes in Analytics driven digital business transformation

The Trend you should ignore at your peril

The Leading Augmented Analytics Platform

The Last Mile

The Next Frontier for KPISOFT to deliver at Hyper Scale

Gartner Research

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What Organizations could learn to do better

Enterprise Performance Transformation

NewsletterContributors

David is an accomplished senior executive and technology visionary with over 16 years of experience in consulting corporate ventures, start-ups and MNCs.

Naveen is the Global Client Partner at KPISOFT, delivering Digital Business Transformation (DBT), disrupting Enterprise Performance Management (EPMS) and driving client success. In his role, he leads the Consulting, Solutions & Delivery to ensure superior client performance.

Prior to this, Naveen was heading Talent & Performance Solutions for India & APAC with Publicis.

Naveen was a Board Director and MD of Global Operations, Arrows Group UK. He was responsible to drive the worldwide globalization strategy for Arrows, create high value solutions for clients and manage a Global Delivery Centre in India

A technology executive with domestic and international experience, operations management, and strategic business planning. As the Chief Revenue Officer in KPISOFT, David oversees the team that contributes to product vision, support solutioning for customers and drive growth of the business in Asia Pacific Region. Prior to KPISOFT, David worked as the COO/ CTO in GoBike Asia, responsible for operations, sales and technology functions. His role at Ambiata involved growing a data science consulting start-up developed and executed Go-To Market strategy which resulted in opening new markets across Asia.

DAVID NG

NAVEEN NARAYANAN

David holds a Master of Business Administration from the University of Chicago and Bachelor of Business Systems from Monash University, Melbourne, Australia.

Before Arrows, Naveen was Global Head, Talent & Performance Management for HCL Technologies, responsible for driving worldwide strategies around Talent Management.

He has over 23 years of rich experience across HR in industries like Hospitality, Retail, Supply Chain, Technology and Banking. Naveen is a seasoned speaker. He was featured as a key note speaker in the United Nations WEP, Linkedin Global Conference among other fora. He has been featured in a Harvard case study on the value delivered to HCL Technologies.

Under his leadership, organisations have received several external recognitions in Human Capital Performance. Naveen is a qualified assessor with the Dubai Economic Department, Dubai Quality Award, certified Executive coach.

Currently he advises several young companies on their growth & diversification strategies. He is also the Convenor for an NGO supporting more than 100 girl children through a 6 years scholarship.

Naveen is an All India gold medalist from IHM Mumbai, holds a bachelor’s degree in Economics and is an alumnus of the prestigious IIM Calcutta.

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A lot has been written about how the technology of Enterprise Performance Management Systems (EPMS) has improved over time or needs to. The trigger for this article is Gartner’s research on buyer behavior.

This is also a piece that attempts to connect companies that are vanishing faster than in previous decades as they are unable to disrupt themselves ahead of the competition. When you connect these two facets, Gartner shows us that Enterprise buyers actually don’t know how to buy digital disruption. They also don’t know how to assess the magnitude of the problem or define whom to partner with on disruption.

This article is focused on the key questions, insights and mistakes buyers/CXOs make as they chart their digital transformation journeys. Our aim is to educate that buyer who can make better choices, thereby saving his or her firm from extinction or oblivion with the onslaught of change in the macro environment.

Digital Transformation has many paths and journeys to undertake. This article speaks about Data Analytics as the central focus and how Augmented Analytics and “actionable insights” drive enterprise transformation.

Why KPISOFT wrote this

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Where do you START?

Few companies have a Chief Performance Officer/EPMO(Enterprise Performance Management Office) who drives the total Enterprise performance, which is about integrating three key parts of an Organization – “Strategy, Operations and People,” and that ensures both Vertical & Horizontal alignment.

The “Power of Alignment” as a concept was introduced in the now famous book by Professor George Labovitz and Victor Rozanski where they explained how Strategy needs to be clearly cascaded to the last person in the company (Vertical Alignment), and internal processes and customer requirements need to be tightly coupled and nimble (Horizontal Alignment). That’s easier said than done until technology is made accessible and available to enable this.

Vertical & Horizontal Alignment is a concept best explained in airplane flying but without the right technology. Imagine flying a plane using Word documents and Excel sheets and a paper map. That’s hardly a plausible scenario. So how do you expect Alignment in an Organization without the right Tech enablement?

Companies even today struggle to have all three parts of the Organization on one single data platform. In fact, for a firm, it’s a dream to know that the sum total of the parts of the efforts adds up to the whole.

The unlock is to have an Augmented Analytics-led Digital Business Transformation (DBT) roadmap. With technologies powered by KPISOFT, we can today claim that Enterprise technology has caught up with consumer-grade technology.

Imagine a technology that has an Omni Channel-like experience – where you as a CXO can walk into an office and an application just pushes a notification to tell you about the top three performers of last week or the day before, and then nudges you to say hello to them. We’ve lived and seen this experience in retail shopping for many years but assume it’s a pipe dream at an Enterprise.

DBT powered by Augmented Analytics - A Competitive Advantage

What does this mean? We define it as an organization that uses Augmented Analytics extensively and systematically to outthink, outperform & outexecute competition.

The above is applicable to supply chains, customer service, customer loyalty, human resources, production facilities or any other operations in your Organization.

Consider some of these examples -- Netflix uses it to predict movie preferences; Marriott uses it for revenue optimization; Walmart uses this for supply chain Analytics; and professional sports teams for choosing the right players. What you see here is that Analytics-based insights are embedded in the core business and are built as a competitive advantage.

Gartner and KPISOFT believe that these secrets are deeply embedded in any organisation that is willing to learn and reinvent. Focus on these three categories:

It is critical that when Organizations start with their Transformation journey, they know these reference points and plot things accordingly.

And the path to Augmented Analytics maturity has four common characteristics:

Analytics directly linked to strategic and distinctive capability The approach to Analytics is Enterprise-wide Senior leadership committed to AnalyticsThe company betting the farm on Analytics based competition

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There are plenty of Innovations that are solved with minimal effort and these are mostly solved in-house given the right culture.

Some parts of transformation goals require serious effort. This requires organizations to be humble, walk on a lot of “sacredly held principles” and most often listen to external view points.

There are some goals that cannot be satisfied with current models. Every retailer, for example, wants to become an Amazon and chances are that almost none will when they move from their current model unless they start ground up.

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Analytical Competitive advantage comes with two key focus areas:

The Exploit needs to be connected to the core strategy and business, and the Explore aspect needs a “test and learn” culture.

Companies need to change their operating structures and models. Data cannot be restricted to flow only within business units or, if I may call it, within fiefdoms. The broader the data access including external benchmarks, the wider the impact of transformation.

Data Science or Analytics is not a department or a function. It’s a way of life for interaction, collaboration and culture. The most common phrase at organizations when reports are shared is “versions of truth” and that is because companies view Analytics like they view their Organization structures - as individuals, regions, and departments.

Professor Thomas Davenport and Jeanne Harris in their Harvard publication on Analytics clearly illustrate in their research that they could not find a single company where Analytics was the culture without the explicit support and sponsorship of the top leadership. They go on to illustrate that point – Reed Hastings of Netflix was a math teacher, Bezos is insanely quantitative and the list is endless.

However, I don’t want to conclude that there is a correlation to being CEO and being Analytical-minded.

Data always needs to be universal as an approach.

Today, the first-mover advantage, hyper-scale, barriers-to-entry are not governed by old rules. They are governed by one fact -- who owns the primary access to data. Look at what John Deere, the Agri equipment company, has done with agricultural data and its platform that almost forces the rest of an industry to integrate into a new ecosystem built around the platform itself.

Companies that have been successful tend to exploit existing measures to a considerable higher degree and are equally early adopters to explore new measures. How many of you use innovative KPIs, Human Capital (HCM) Benchmarks, and connect statistical modelling and predict linkages to success?

Are practitioners too focused on getting the process right and are there almost none who are shouldn’t we singularly focused on getting this right to improve Enterprise Performance measures?

Risk-taking as a culture is covered in a lot more detail in the next section as an attribute, but consider the best-in-class and how they experiment and explore. Netflix may use Analytics in its core strategy, but explores and tests its advertising using Analytics. Casino chains explore and experiment pricing and slot machine placement for revenue maximization.

Winners in Augmented Analytics do 2 things right -

Explore & Exploit

Venture Capitalists, as a tribe, spot the best of disruption and it may be worthwhile to examine the traits and characteristics they show that mature organizations almost never have.

“Beracha, before he retired as CEO of Sara Lee kept a sign on his desk “In

God we trust, all others bring data” – a quote originally attributed to Edward

Deming, the guru of quality.”

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The THREE mistakes in Analytics-driven digital business transformation

4 THINGS TO LEARN

WHY “EXPLORE” CULTURE NEEDS FOSTERING

The first fundamental difference between you and this tribe is their insane appetite for risk. On the Pareto curve, a VC succeeds maybe 20% of the time which also means that 80% of their flops are like a crash. How many Organizations allow this latitude of failure in explore culture mindset? Organizations want the exact opposite on the Pareto curve.

VCs never spray and pray. They are laser focused on a few ideas and try to build them. Add to it, they don’t look for normal distribution of performance where two companies perform say at 2x, some at 1x and some at 0.5x. It never happens this way. However, when you review a project charter of a digital transformation agenda, every CXO demands every project will be successful.

Where to bet the farm? The way transformation works, about 20% of your ideas will/should be wildly successful, outperforming the others by 10-20x times and carrying the entire organization from one phase to another. How ready are you for this level of wild experimenting and bet the farm on that 20% ideas?

The fourth law that makes VCs thrive is – they build great teams that work and stay together, and are insanely passionate about an idea. When you share this, you are like co-conspirators (reference – Tokien from Lord of the Rings).

We have seen that Transformation requires a different kind of thinking and not an extrapolation of the past. Today even years of experience does not matter, but it’s the ability to ask the disruptive questions and deploy the solutions that makes all the difference.

Let’s look at a tangential example and see why this group of people get transformation right as a tribe. How do they balance explore and exploit well?

Venture Capitalists, as a tribe, spot the best of disruption and it may be worthwhile to examine the traits and characteristics they show that mature organizations almost never have.

Creating a committee where price becomes the single biggest determinant of a decision. We still find that, to push democracy, different people are brought in to find a common minimum acceptable program or a lowest common denominator.

These committees or an individual tend to write needs on the basis of tools and processes that are currently in use today and almost rarely from an explore and experiment standpoint.

It’s almost rare for someone to seek capabilities on change management and the focus on Culture as the silver bullet is assumed as Technology and determinant is Price.

“It’s about collecting information of 200 million customers and on that basis making a series of long term

decisions for the business.”

Richard Fairbank, CEO, Capital One

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5 stage Analytics-Based Digital Transformation – The KPISOFT Model

How to build a Buy Strategy – The KPISOFT Model

Stage

CAPABILITIES

Organization

Human

Technology

Do you know your performance drivers – KPIs / OKRs?

Senior Management commitment?Is there a distinct Data Culture?Does everyone believe in Data democracy?

Augmented-Analytics based KPISOFT Enterprise PerformanceMachine Learning in a box & self service like Intuceo.com

Is Organization distinctive strategy clearly articulated?Is there a clear Performance Management & execution program?

KEY ELEMENTS

ObjectiveDistinctive

Capability & Levels of Insight

Metrics / Measure of success /

Value

Questionasked

Analytically deficient

Flying Blind What

happens in our business

Getting “accurate”

data to improve

None1

How can we understand our business better

Using analytics to improve a function

Opportunistic but not linked to company’s

strategy

ROI of the function

LocalAnalytics

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Can we extrapolate

trends?

Using analytics into core strategy

Data LakeFuture

performance prediction

Analytical Aspirant

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How can analytics help innovate and differentiate

Analytics for differentiation

Enterprise Wide, Roadmap clear and on a

journey

Analytics drive

performance and value

Analytical Transformation 4

What’s next & beyond the

possible

Master of Analytics

and competing

Sustainable advantages

Enterprise wide

Primary driver of value and

performance

AnalyticsCompetitive Advantage

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KPISOFT recommends 5 characteristics to Augmented Analytics-led Digital Transformation

Do not play Copy Cat -- Analytics and Insight journeys between 2 companies cannot be duplicated.

Adaptable – Analytical organizations like Amazon cross internal fiefdoms easily. Culture is key.

The new age Renewable – The competitive advantage once built needs constant improvement and reinvestment.

Transformation Truths organizations refuse to see

The Living Company – The 4 tough questions to answer

Unique – There is no single path to follow and needs to be bespoke for every firm and unique to its culture.

Exploit it better – Even in industries like Finance or Airlines where data is in the DNA, some companies exploit this better than others.

A disruption / transformation messed up at the start cannot be fixed later. Get it right the first time.

Do we only react when threatened? Why did it take a behemoth like Disney so many years to start and copycat streaming service like Netflix?

Who in your firm is looking at “adoption and utilization” rates of enterprise technologies as passionately as Facebook for example would look at, with their consumers?

Does your company measure yourself on a “disruptive index”?

Are rewards of CXOs driving the above agenda linked to such metrics?

Very few companies look for “insane rates to transform” and keep seeking glide paths. They wait till the organization is threatened with extinction with the exception of a few.

ERPs will vanish with the advent of consumer Apps and ease of integration and data flows. ERPs are for safe bets, and not for transformation journeys. Choosing ERP systems of transactions or record will not deliver a system of intelligence or insight.

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At KPISOFT, we believe we bring you a set of questions and experiences that no one would have asked before.

Our disruptions drive Digital data democracy, turning Organizational structures and operating models on its head.

Look at some examples of how KPISOFT has transformed the learning function in organizations by removing the monopoly of a department and building it as a continuous experience on a mobile platform. Employees have performance insights coupled with appropriate learning bites suggested by the Machine (we refer to this as Human+).

KPISOFT transformed the procurement function in the HORECA industry of Asia by providing intelligent & actionable insights removing the monopoly of a department. Today our HORECA clients save 5-20% of their procurement costs using this platform

KPISOFT is a strong advocate that any technology which cannot speak to you notifying, recommending a nudge represents legacy. The metaphor used here is of “silent movies” as legacy, then why believe ‘silent analytics’ as futuristic?

This hybrid brain is what KPISOFT refers to as Human ++ being delivered to Enterprises. Machines make humans better performers.

Let me be clear that none of these principles are new. They have existed in management and adapted to the Digital world:

The Burning Platform Cliché - we find companies spend very little time to “Fall in love with the problem.” Look at Uber, which believes it is solving the problem of shared transportation for the world. How can your company create this common view across the organization? That’s what may also be referred to as a “shared purpose.”

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Digital Business Transformation has taught us even more to put the user (Customer or Employee) at the centre – and map out the experience journey. In the analog world, this was referred to as “moments of truth” where companies like SAS Airlines in the mid 80s transformed. The sharpest quote from the then CEO was “we don’t fly planes, we fly passengers”.

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Once you are user-centred, then it is about how to make the organization “iterative.” Therefore, don’t love the solution more than the problem -- “Produce-test-improve.” In the digital world, this is called sprints. When laid out that way, it sounds the most obvious. As a tip to the buyer, you should worry when partners and vendors peddle solutions as silver bullets when all they can claim is their iterative experience.

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Companies spend too much time on selling the solution – the operating model, the process. and so on.

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CXO Sponsors should actively look for green shoots innovation and scale them rapidly. The future of talent hunt happen this way. This can only happen when you “invert the pyramid” where the CXOs work for the “value zone” employee. These insights are explained a lot more in the famous book “employees first, customers second.”

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Dashboards should talk to you, predict and recommend next steps, spot the problem ahead of time. They should also help you collaborate in squads and projects and not legacy hierarchies, ask queries on Alexa/Siri where bespoke analysis is presented, and hyper-personalize to your context, role and performance. KPISOFT does all this and more.

Gartner predicted Augmented Analytics to be the Number ONE digital trend of 2019 and KPISOFT is fundamentally rooted to Augmented Analytics.

To understand “augmented analytics,” we must first understand the problem it solves. That means we must understand why generating insights from data remains a huge challenge for almost all businesses.

In fact, vanilla data, by itself, is totally useless to your business.

For example, your sales data might reveal that your profit is decreasing by 10% from last month. But what does that really mean to you? Is this decline an industry trend? Is it because one of your advertising channels is not working? Or is it because of other reasons?

That’s why you need to go deeper into your web analytics, e-commerce, and social media data to uncover what resulted in the decline of your revenue. Then you need to place those changes into a business context, and identify the ones that you can act on immediately.

Going back to the declining profit example, you might end up realizing that your social ads are 10% less effective than the previous month, and that you need urgent action to optimize your ad spend.

Now, that insight is actionable. And that is because it connects directly to an action you can take to solve your business problem. These actionable insights are extremely helpful because they serve as a guiding light for what you should prioritize in your business.

What augmented analytics does is it relieves a company’s dependence on data scientists by automating insight generation through the use of advanced machine learning and artificial intelligence algorithms.

KPISOFT through its self-service, self-learning Machine Learning platform provides this in many industries, with examples like Lockheed Martin and Johnson & Johnson.

The trend you should ignore at YOUR PERIL

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KPISOFT’s Leading Augmented ANALYTICS PLATFORM

KPISOFT uses machine learning and artificial intelligence algorithms to automate the data analysis process. The platform can automatically discover data, prepare and analyse with minimal human intervention.

It is important to distinguish the new wave from KPISOFT with existing systems out there that aid data analysis. Yes, numerous solutions exist that can “support” data analysis by providing visual aids and by making analytical tasks easier. Instead, augmented analytics automate difficult tasks that ordinarily still require data scientists.

Insight generation -- Loads of facts do not equate to an insight. For example, knowing that regional sales have increased is useful, but knowing why this occurred can make all the difference. Machine learning algorithms help automate the process of understanding what exactly it is your data is telling you about your organisation.

Qualitative Analytics – the words sounded like an oxymoron until recently when it was always assumed that Analytics is only with numeric data. Platforms like KPISOFT read any quantitative data source and provide the “why.” So an insight may say that satisfaction dropped by 30% and the primary reason for the same is because the checkout button is cumbersome.

Pattern recognition by KPISOFT -- Just because you have a data lake or visualization tool, does not mean that it will deliver actionable insights. Augmented analytics can automatically detect strong data signals Vs Empty Noise.

Finally, we have an augmented analytics platform, almost a first of its kind that is designed to deliver curated and contextual content in real-time.

New age technologies like Appsee are experimenting with touch heatmaps to record App journeys. These features allow you to better understand user experience.

Today’s data scientist is in a box and a platform (Intuceo.com).

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Enterprise Transformation through people transformation is how the future of performance management will be managed. This was a term coined by Deloitte where Analytics meets Behavioural Economics.

Just delivering actionable insights sometimes does not lead to the desired actions. Companies or nations are looking at Behavioural Economics at scale and the Nudge Theory made famous by Nobel Prize winning economist Richard H. Thaler.

KPISOFT platform uses the Nudge and Positive Behavioural Reinforcement theory when pushing Augmented Analytics insights to an employee. Actionable insights does not guarantee action. A nudge significantly increases its probability of action in the desired form.

Positive Reinforcement is a subset of behavioural economics and a way to shape culture and human behaviour. It is a subcategory of design and behavioural economics concerned with how design can shape, or influence human behaviour. It is designed to do one of two things.

It is designed to do one of two things -- A nudge is to guide the “fast brain” into making the preferred decision automatically, and Insights is to direct people to engage their “slow thinking” brain and make a calculated decision.

The LAST MILE

“Humans have limited time and brainpower. As a result, they use

simple rules-of-thumb—heuristics—to help them

make judgments.”

“Psychologists tell us that in order to learn from experience, two

ingredients are necessary: frequent practice and immediate feedback.”

“Because learning takes practice, we are more

likely to get things right at small nudges than large

stakes.”Richard H. Thaler,

economist and Nobel Prize winner

Richard Thaler Richard Thaler

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The Next Frontier for KPISOFT to deliver at HYPER SCALE

An Accenture–Economic Intelligence Unit study points out that >90% consider management of intangible assets and driving analytics on these parameters is where new competitive advantage lies. Examples include insights on Human Capital, Intellectual Capital, Brand, R&D and so on.

Companies that pay disproportionate attention to new metrics, data-linked KPIs, interrelationships and scorecards are the ones who are poised to win. Technologies like KPISOFT power such approaches.

They will continue to lead the industry into the future, powered by KPISOFT.

Companies need to adopt the this motto that – if it’s worth transforming and disrupting, then it’s worth doing it analytically. These companies will focus both on a data culture and technologies. ‘Quant Jocks’ in the company will work with ‘Quant Amateurs’.

Augmented Analytical-based Digital Transformers will find newer ways to stay competitive ahead. They will know their best customers, how much to charge and win through continuous data modeling. They will know the most effective marketing campaigns, better than their competitors. Their customer loyalty will far exceed their competition using actionable nudges. Their supply chains working on deep insights will be ultra efficient using platforms like Zeemart. They will have the most productive people in the industry. They will be able to predict and diagnose problems faster than most using Machine Learning.

Kaplan and Norton’s Balanced Scorecard framework will be complete when companies start measuring these intangible assets as part of their KPI framework as normally as the others. The technology is available with KPISOFT to measure these via unstructured information. However, are buyers ready to invest into this new realm?

Connect with KPISOFT

Follow KPISOFT on

Follow @kpisoft on

Like KPISOFT on

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Traditional, IT-driven approaches to analytics and business intelligence can’t deliver the value and benefits required by digital business today. Gartner’s research in 2019 will help data and analytics leaders take a business-driven approach to analytics technology decisions and investment.

Scope

Analytics and business intelligence (BI) solutions entail systematic, sustained action to plan, select, deploy and manage analytics technologies that will deliver business outcomes and business value.

Gartner’s analytics and BI solutions research agenda for 2019 focuses on just such a business-driven approach. It covers three core topics:

Applied analytics: Building a business analytics portfolio that focuses on use cases specific to industries and business processes in order to drive improved business decision making at all levels.

Business analytics: Equipping business users with the foundational analytical models and simulations to understand present realities and predict future states by developing a spectrum of capabilities spanning analytic workflows, using technologies such as data mining, predictive analytics and statistics.

Machine learning and data science: Using sophisticated quantitative methods such as statistics, machine learning, simulation and optimization to discover patterns or outcomes that humans would be unable to identify in large structured and unstructured datasets.

Some content may not be available as part of your current Gartner subscription. Contact an account executive if you wish to discuss expanding your access to Gartner content.

Analysis

Research from Gartner:

Analytics and BI Solutions Primer for 2019Initiatives: Analytics and BI Solutions

Analytics and BI Solutions Overview

Gartner (February 2019)

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Digital business demands are exceeding the capabilities of traditional approaches to analytics and BI, which are centralized, IT-driven and technology-oriented. Today, business stakeholders at all levels need insights that are more complete, current and relevant than those offered by past practices. They want business-driven analytics, which means they have what they need to apply analytics themselves, using solutions that meet their business needs. They want solutions that are pervasive, timely and integrated across business units and processes. Most of all, they want solutions that deliver business results.

But merely selecting a modern analytics and BI tool aligned with an organization’s existing IT vendor landscape may not deliver the needed analytics outcomes. Business outcomes, priorities and value — not technology — should drive adoption of analytics technology. Creating an interactive dashboard, by itself, will not achieve a business outcome, such as reduced customer churn. That outcome requires business actions supported by a cluster of analytics outcomes, which in turn depend on diverse roles and skills, discrete analytics capabilities and associated data capabilities. Without a business focus, even investments in potentially transformative technologies, such as data science, machine learning and artificial intelligence (AI), will be squandered. Technologies and tools must be embedded in business processes and business decisions.

Data and analytics leaders must start by focusing on their organization’s most important and urgent business outcomes. From these outcomes, they can derive the requirements that will guide the creation and deployment of effective analytics and BI solutions. These are the solutions that will drive business value. They will create the analytics capabilities needed to transform their enterprise and its ecosystem, empower employees through greater insight and embrace the complexity of the modern digital business environment (see Figure 1). When business outcomes drive analytics solutions, specific decisions about analytics and BI tools, platforms and technologies will be accurate and cost-effective and will deliver business results.

Topics

Creating analytics and BI solutions that directly enable business outcomes and drive measurable value is a complex but vital task for data and analytics leaders. But such a business-driven approach to analytics is still rare in many organizations.

Being business-driven implies delivery of the analytics technology that users need, not simply of the capabilities offered by the incumbent vendor. It implies accessing the data needed to address specific business problems, instead of the data most easily provided and analyzed by existing tools. It implies development of business users’ analytics skills to complement investment in centralized pools of analytical specialists. It implies the creation of new practices that often contrast starkly with existing processes for managing IT operations.

Our research centers on the following topics:

Applied Analytics

The use of data and analytics is becoming evermore pervasive across business domains, but most of it is carried out in isolated “silos.” These silos create problems and costs, such as duplication of data and wasted effort on similar analytics processes across the organization. To overcome these problems, data and analytics leaders must harmonize decentralized and narrowly domain-focused analytics efforts into a strategic discipline — applied analytics.

Questions Your Peers Are Asking

What are the top trends, use cases and business benefits for customer analytics?

What are the key trends in, and the vendor landscape for, customer, web, text and social analytics?

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Business Analytics

Every business has a broad range of use cases that can benefit from analytics and BI solutions, yet too often IT-driven analytics initiatives are narrowly focused, and therefore miss the mark in terms of business value. By starting with the desired business outcomes, data and analytics leaders can create a complete portfolio of tools and platforms that deliver analytics capabilities targeted directly at those outcomes. A business-driven approach entails using self-service analytics initiatives to deliver insights to business users and improve their decision making at all levels.

Questions Your Peers Are Asking

How can we impact the business and drive performance with analytics?

How can we select the right analytics and BI solutions for business users?

How can we empower business users with self-service analytics without losing control and trust in information?

What are the key trends and innovation opportunities in business analytics?

Machine Learning and Data Science

Developing a mature machine learning and data science capability that targets critical business problems and opportunities is vital to creating insights and optimizing business processes. Data science, including AI technologies such as machine learning and natural language processing, offers a powerful capability to deliver relevant, timely insights to business decision makers. Using sophisticated quantitative methods such as statistics, machine learning, simulation and optimization, organizations will turn data into action and business impact.

Questions Your Peers Are Asking

How can we introduce and optimize data science and machine learning capabilities for analytics solutions?

How can we optimize the delivery of data science outcomes at the pace required by the business?

How can data science be used to optimize decision making in organizations?

How can we apply real-time analytics on business processes to support digital transformation?

Planned Research

Research identifying best practices for deploying and managing self-service analytics and citizen data science.

Research on how to optimize the delivery of data science outcomes at the pace required by the business.

Research to help data and analytics leaders develop and implement the tools, infrastructure, skills and practices needed to deliver continuous intelligence in support of digital business.

Research that presents the best practices, technologies and vendors for streaming analytics, deploying digital-twin architectures and enabling the IoT.

Reports to help you choose the right analytics tools and technologies for your business needs.

Fresh editions of “Magic Quadrant for Analytics and Business Intelligence Platforms” and “Critical Capabilities for Analytics and Business Intelligence Platforms.” These will help you create a more complete analytics portfolio aimed at delivering business value.

Research to help organizations deploy emerging analytics and BI solutions, such as those using augmented analytics, natural language processing, storytelling and automated machine learning. This research will include discussion of the tools, skills and best practices needed for success.

Research on how to introduce and optimize data science and machine learning capabilities for analytics solutions.

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Research identifying best practices that enable organizations to develop the full range of relevant analytics capabilities within a coherent architecture. This research will explore cloud deployments, streaming data, open-source technologies, digital twins, AI integration and other impactful technology trends.

Research explaining, and describing how to implement, continuous-intelligence solutions for the most relevant purposes, using analytics solutions such as predictive and prescriptive modeling.

Guidance on how to develop data science as a growing, operational capability applicable to key business outcomes.

Research describing the top trends, use cases and business benefits for customer analytics.

Research analyzing key trends in, and the vendor landscape for, customer, web, text and social analytics.

Suggested First Steps

“Gartner Analytics Evolution Framework”: How to design a solution roadmap using an analytics framework that focuses on achieving business outcomes.

“Augmented Analytics Is the Future of Data and Analytics”: Augmented analytics uses machine learning and AI techniques to transform how analytics content is developed, consumed and shared.

“A Roadmap for IT to Help Drive Machine Learning”: Lessons learned from hundreds of inquiries and a recent data science survey outline best practices for driving successful machine learning projects.

“How to Drive Value From Customer Experience Analytics”: How to make the strongest business case for prioritizing customer experience analytics and drive strategic business value.

“Create a Centralized and Decentralized Organizational Model for Analytics”: The optimal organizational model for analytics solutions requires a centralized team that works with business-unit-based decentralized teams.

“Select the Right Analytics and Business Intelligence for the Right User and Use Case”: No one vendor offers all the best capabilities for your analytics and BI portfolio. Optimize your investment strategy to match the right tool to the right user.

Gartner Data & Analytics Summits: A global series of conferences bringing together data and analytics peers with Gartner analysts for networking opportunities, research findings and in-depth discussions.

Tools and Toolkits

“Toolkit: Gartner Analytics Evolution Framework”: Use this Toolkit to design an evolution roadmap for business analytics and data science environments. Gartner’s framework connects data and analytics technologies to desired business outcomes.

“Toolkit: Gartner Analytics Atlas”: The Gartner Analytics Atlas helps you understand and navigate the complexity of business analytics and data science technologies with a view to synergizing capabilities for different analytics initiatives within your organization.

“Toolkit: How to Get Started With Analytics”: This Toolkit gives data and analytics leaders a step-by-step guide to starting and maturing an analytics initiative.

“Toolkit: Track How Well Your Analytics and BI Program Serves Its Users”: This Toolkit includes a five-question survey that you can administer to analysts and decision makers to evaluate how well existing analytics and BI strategies meet their needs, and to track the progress of these strategies over time.

“Toolkit: Analytics and BI Platform RFP”: This request for proposal template gives you a head start in defining and prioritizing required capabilities when selecting a modern analytics and BI platform vendor.

“Toolkit: RFP for Data Science and Machine Learning Platforms”: These proposal templates for data science and machine learning platforms help you define your objectives and capabilities in order to select the right vendor.

18

Related Priorities

Initiative Name DescriptionMarketing Data and Analytics Data and analytics have become the foundation of

marketing, driving efficiency and effectiveness through better data collection, modeling, optimization and greater relevance to the consumer.

Digital Workplace Program A digital workplace program is a business strategy to boost workforce digital dexterity through an engaging and intuitive work environment.

Data and Analytics Strategies Organizations need to set strategies and practices that fully exploit the combined forces of data, analytics and AI to deliver business value both within and beyond their business.

CRM Strategy and Customer Experience CRM and CX are separate but overlapping and interconnected enterprisewide initiatives garnering a high degree of executive support and scrutiny. They are often key elements of digital transformation.

Customer Service and Support Technology Many technologies and best practices are needed to create a leading customer service and support organization and its associated operational, customer experience and employee experience aspirations.

Sales Technology The sales technology research agenda focuses on the selection, delivery and maintenance of tools that optimize how sellers and managers conduct their daily sales processes.

Gartner Research Note G00709926, Joao Tapadinhas | Carlie Idoine, 5 February 2019

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