Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often prove unreliable. For a long time, IT has been tasked with building systems that could represent a single source of the truth, which has been nearly impossible. For the first time in history, we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable.
In this report, IIA Expert Geoffrey Moore addresses the Digital Systems Maturity Model, which includes four systems:
Systems of record occupy the bottom step in this maturity model, having been prevalent for closing in on three decades
Systems of engagement are relatively new to the scene, be they in service to marketing automation, customer service, or sales enablement
Systems of intelligence are just emerging onto the scene, having become highly visible in the leading disruptive enterprises who are currently eviscerating their competition
At the highest level, just coming out of the labs in most cases, are systems of autonomy
One of the continual challenges for analytics leaders is measuring the work of their analytics groups and communicating the results to business leaders. Sound measurement and transparency form the foundation for strong working relationships, adequate funding of analytics, and trust in the analytics organization.
The work of analytics organizations – or any groups chartered to improve enterprise performance by adding capability and introducing change – can be assessed comprehensively through four questions:
Intent: Are we working on the right things?
Performance: Are we doing those things well?
Results: Are the outputs being put to good use and creating value?
Health: Are we maintaining and building the right capabilities to meet business demand and perform better in the future?
This report examines how analytics organizations measure intent, performance, results, and health.
Does the development of enterprise analytics capabilities really drive superior company performance?
As IIA works with clients from around the world, we are often asked this question. Of course, we strongly believe that it does. This very premise is at the center of our founding and our mission. But is this belief supported by actual data? The fact that we believe analytics drive performance isn’t enough. The claim must be validated with real data and analytics that support the claim.
This report provides a range of such supporting evidence, using IIA’s proprietary analytics maturity data – from 74 leading companies like Amazon, Apple, Netflix and Google – and publicly available financial and company data, to illustrate the positive association between analytics maturity and superior company performance.
Each year, the International Institute for Analytics takes time to focus on the latest analytics trends and the most pressing analytics challenges currently facing organizations.
We gather the basis for our predictions from our day-to-day work supporting and advising analytics leaders and programs. Our insights arise from the breadth of expertise and cross-industry perspectives we receive every day from our clients, partners, and members of the IIA expert network.
This is our 8th annual look forward into the upcoming year. The annual Predictions and Priority research brief and the associated webinar are among IIA’s most popular content of the year. For the last several years, we have augmented our predictions by also providing some specific priorities for leaders as they navigate the many aspects of elevating analytics capabilities in their enterprises. This year, each priority provides specific guidance as to how to best prepare for, and adapt to, its corresponding prediction.
The purpose of this report is to summarize the key elements of DELTA and Five Stages of Analytics Maturity, and discuss how these two frameworks can be used to understand analytical maturity in your organization.
The DELTA Model and Five Stages of Analytics Maturity have become the industry standard frameworks for assessing analytics maturity. The DELTA Model was developed in 2010 by Tom Davenport, Jeanne Harris and Bob Morison in their book, Analytics at Work: Smarter Decisions, Better Results. The Five Stages of Analytics Maturity was developed in 2007 by Tom Davenport and Jeanne Harris in their book, Competing on Analytics.
The five elements of a successful analytics program, as stated in Analytics at Work, are:
- D for accessible, high quality data
- E for an enterprise orientation
- L for analytical leadership
- T for strategic targets
- A for analysts
These five elements must be in alignment for organizations to succeed with their analytics initiatives.
This spring, VP David Alles attended Strata + Hadoop World in Silicon Valley. Learn about the top takeaways from the conference. This event summary also includes machine learning and artificial intelligence use cases.
Google Cloud Platform (GCP) has become one of the key strategic focus areas for the company and this commitment was on display at Google Cloud Next, Google’s recent conference held in San Francisco from March 8-10, 2017. The sold out conference had 10,000 attendees and showcased the latest GCP developments.
IIA Vice President David Alles’s executive summary outlines key takeaways from the conference and how Google Cloud Platform is competing with Microsoft Azure and Amazon Web Services.
Download the 2017 Winter edition of our semi-annual Analytics Journal for an inside look at featured research, expert profiles and new offerings from IIA.
Analytics can no longer be considered an optional capability for businesses that strive to be competitive in today’s environment. In working with organizations across a number of industries, one of the critical components of any successful program or initiative is driven by finding the right people to lead and participate in the program.
In this blog series, IIA Expert Network member Emilie Harrington discusses the various roles people play in an analytics program; people are always the most important asset of any organization, the life-blood of a company.
The intent of this blog series is to provide guidance and information for those who are just beginning to explore the idea of developing an analytics program. Harrington discusses topics such as the organizational structure, roles, team or job family design, skills/knowledge/ability (SKAs), and recruiting and retaining analytical talent.
Learn how to build an effective analytics team for your organization.
As business analytics continues to gain momentum and increase its impact on organizations, the challenges facing analytics leaders continue to evolve. While the need to establish credibility and build new data analytics teams dominated agendas just a few years ago, many organizations now focus on expanding capacity, operating more efficiently and measuring the return on analytics investment. With continuous, rapid advances in technology and automation, knowing which trends to pay attention to is more important than ever.
With that in mind, the International Institute for Analytics published its annual report of top predictions and priorities for 2017. Each year, IIA shares its perspective on the year ahead in the world of analytics, presenting viewpoints from leading analytics practitioners, executives and thought leaders. This year IIA gathered feedback from more than 200 analytics executives and our broad network of unbiased analytics experts.
Learn about the major trends and pressing priorities analytics professionals should pay attention to in 2017.
As organizations elevate their analytics capabilities, one often overlooked challenge of analytics maturity is the balance between striving for more advanced capabilities and strengthening core business intelligence competencies. This research report, written by the International Institute for Analytics and sponsored by SAS, explores the relationship between Business Intelligence (BI) and Advanced Analytics (AA) as well as their adoption among large organizations. It reveals that BI is adopted and used more widely than AA, but companies that use BI across their entire organization are also more likely to implement AA across their entire organization.
This report provides answers to the following questions:
- Where are organizations at in their adoption of BI and AA, and how do they execute the two disciplines?
- How do organizations view their current BI and AA capabilities, and where are the largest gaps between importance and performance?
- What are the key themes and barriers that hamper adoption of BI and AA?
- How do organizations plan to invest in BI and AA over the next few years, and which emerging capabilities might be implemented?
The era of impact with analytics has arrived, and the business world is taking notice. Google, Facebook, Amazon and Netflix have all built hugely successful businesses around algorithms feeding on Big Data. The emergence of these “digital native” companies has led to dramatic upheaval in brick-and-mortar industries that once seemed invincible. The lesson here: No company today is safe from the threats posed by the analytics prowess of their direct and indirect competitors.
This report provides an overview of our research into the analytics maturity of 50 industry-leading companies across 12 industry segments, seeking out differences in enterprise analytics capability.
This report, advancing our 2014 research, assesses the maturity of both midmarket and large enterprises in implementing analytics solutions. It shows that analytics and Big Data is expanding at an impressive pace within enterprise operations, business processes, and decision-making.
It takes a lot more than technology and talent to make analytics work. For analytics programs to thrive and make a positive difference within your organization, it is essential to structure your analytics team the right way.
As firms devote more and more resources to their data and analytics programs, IIA has been tracking the evolution of leadership roles that will be required – Chief Analytics Officer (CAO), Chief Data Officer (CDO), and other permutations of these positions.
As business plans increasingly call for reliance on Big Data, and users are becoming more proficient at making use of data through analytical tools, Hadoop has emerged as a popular technology for consideration. But many companies still wonder exactly where and how to leverage Hadoop in order to be successful with big data.
Defining Analytical Excellence in Healthcare: The DELTA Powered Analytics Assessment Benchmark Report
Healthcare providers amass staggering volumes of data from Electronic Medical Records, billing and insurance reimbursements, yet collecting the data is no guarantee of improved clinical or financial outcomes. Our research shows what steps top performing healthcare provider organizations take to exceed expectations and excel in data and analytics.
The last decade has ushered in tremendous changes and challenges for businesses. CEOs juggle business models, strategies and technologies constantly, trying to keep their firms ahead. This research shows that advanced analytics - defined as predictive and prescriptive analytics rather than simple reporting - is increasingly being adopted by both midmarket organizations and large enterprises in an effort to gain a competitive advantage in their markets.
Enrollment in customer loyalty programs is growing at a feverish pace. But most businesses struggle to get the most value out of these programs. Drawing on interviews and survey responses from more than 300 executives, this report shows that companies with the strongest loyalty programs rely on analytics to drive strategies and create measurable business impact.
Healthcare providers have significant work left to do in order to get the most value out of the data they collect. While most hospitals and other providers have implemented components of an EMR, gaining access to large amounts of data, those assets have not been put to best use. These are among key findings of “The State of Analytics Maturity for Healthcare Providers.”
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Analytics 3.0 is the stage of maturity where leading organizations get measurable business impact from the combination of traditional analytics and big data. Get our free eBook, by IIA Research Director Tom Davenport.