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What's behind this myth?
The incredible growth in the volume of data an organization collects is met by an equally astounding number of data sources. According to a recent survey, companies draw from an average of 400 different data sources to feed their BI and analytics tools, and 20% of the organizations surveyed reported having
1,000 or more data sources.
A traditional approach to data analytics and BI suggests an organization must bring all of this data together in one place to analyze it. But how? Cloud data warehouses are the approach many organizations use, but time-consuming data preparation, maintenance costs, and data analysis limitations are holding them back.
Companies draw from an average of 400 different data sources to feed their BI and analytics.
What's behind this myth?
Organizations must capitalize on their explosive data growth. They need an analytics solution capable of handling a large volume of data to do so. But organizations believe they must accept the limitations of BI and analytics tools that rely on in-memory engines to analyze it. These engines are simply not built to handle large data sets. So organizations are instead trained to break up their data and feed it in simplified, bite-sized pieces to the BI engine. Data is extracted and moved elsewhere, whether to Excel or another tool, for transformation before being re-introduced to the BI tool.
Enterprises will generate and manage 60% of the 163 zettabytes of data by 2025.
What's behind this myth?
In an ideal world, decision-makers across an organization could access a dashboard of all data to gain a true 360-degree look at the trends and factors driving their decision-making. In reality, people have come to expect dashboards that can only display one source of data. So instead of a single dashboard, organizations end up with dozens of dashboard views of all different data sources making analysis and insight difficult to come by.
While this limitation is widely accepted as accurate, what’s also true is that answers to complex questions often require more information from more sources. A common workaround is to extract data from core systems and bring it together in a single dashboard. However, this introduces data latency and replication risks.
"Reports and dashboards are still the most common types of analytics with nearly all organizations (97% and 96%, respectively) indicating they are important or use them currently."
Ventana Research Analytics and Data Benchmark Research, 2022
What's behind this myth?
Many BI tools force people to replicate data from source locations into a data silo or warehouse before they can analyze it. The idea is to copy data from various enterprise sources, clean it, and have a separate, controlled environment to store the data where it can be accessed by the BI tool. In addition to security that is implemented on the data warehouse, some analysts find themselves building content multiple times to go against different data sets to restrict what access is available to the end-user.
Replicating the data preserves the structure of the original source but adds extra steps and complexity to the process as organizations need resources and procedures to maintain the data’s consistency. Is this necessary? Is this smart?
"Data preprocessing such as cleansing and formatting it for analysis is time-consuming. Some estimates suggest that this can account for 80% of the effort in data analysis projects."
“Analysts spend the bulk of their time on manual tasks such as preparing data for analysis (47%) and checking quality and consistency (45%) in the data rather than doing actual analysis.”
David Menninger, Ventana Research
What's behind this myth?
When adding new technologies to an organization’s stack, the IT team has traditionally played a critical role in evaluating and selecting new tools. This role often extends into the actual use of the technology at hand as IT helps implement, integrate, and train staff on the new tool.
When it comes to business intelligence, organizations have no shortage of BI tools at their disposal, but many find people lack the skills to leverage these tools for basic reporting, let alone make strategic decisions with them. What often happens is the IT team, which was responsible for implementing the BI tool in the first place, becomes the bottleneck, and business people rely upon them to pull reports and make changes to reports on a regular basis. This routine practice delays the availability of timely data and disempowers people from embracing and fully leveraging their organization’s data.
67% of managers and executives are not comfortable accessing or using data from their analytics tools.
What's behind this myth?
Data science is an exploding field, fueled by organizations seeking actionable insights from the vast amount of disparate data they have collected. Hiring data scientists — those trained to process, model, analyze, and extract insights from this data — is often viewed as a critical first step. However, many organizations aren’t prepared to accommodate the specialized environments
they require. What’s more, organizations shouldn’t expect data scientists to use buiness intelligence tools. So, despite an urgent need for more advanced capabilities, hiring data scientists may only make it more difficult to reap the benefits of data investments.
The U.S. Bureau of Labor Statistics predicts the data
science field will grow about 28% through 2026.
90% of respondents say data-driven decision-making is a growing priority.
What's behind this myth?
Despite the powerful potential and colossal investments made in business intelligence tools for self-service analytics, the actual usage of these tools is far less than anticipated. What’s more, most available business data — more than 80% — is not used to make decisions (even as it has grown exponentially in recent years). And employees — again, more than 80% —don’t have access to analytics.
Common challenges impacting the scalability and adoption of BI tools include tools that are simply too challenging and time-consuming to use and an overall lack of appropriate analytical skills required to effectively use the organization’s chosen technologies. As such, it is well-accepted that only a select few within an organization actually “own” the business intelligence function.
“The average adoption rate of business intelligence (BI) among employees in mid to large companies is 15%.”
What's behind this myth?
AI is defined as the development of computer systems that can handle tasks previously performed only by humans. While many technologies claim to be AI-driven, the reality experienced by many people is that there is still much to be desired when replacing human actions and thinking. People still find themselves structuring their data, analyzing data, extracting insights, and making decisions from scratch. Where’s the AI to truly support decision-making? As far as managers and executives can see, it’s all just a dream.
More than 95% of enterprises consider AI technology to be important to their digital transformation efforts.
451’s “Voice of the Enterprise: AI & Machine Learning Use Cases 2021”
“By 2025, 9 in 10 analytics processes will be enhanced by artificial intelligence and machine learning to streamline operations and increase the value that can be derived from data.”
David Menninger, Ventana Research
What's behind this myth?
It’s only normal to think common practices are best practices. In the case of analytics tools, it’s common for organizations to take a department-by-department approach to analytics. One tool for finance, another for marketing, another for sales. One for data prep, one for data science, one for reporting. Before you know it, each department or function pulls data into its own tools, believing that this compartmentalized approach is best.
This behavior is predicated on the belief that no single analytics tool can possibly serve all organizational needs. On average, organizations own four or more different analytics tools, a recipe for isolated decision-making, incomplete and inaccurate data, and ever-diminishing adoption.
25% of organizations use 10 or more BI platforms, 61% of organizations use four or more, and 86% of organizations use two or more.”
What's behind this myth?
The ability to capitalize on data and analytics is a fundamental characteristic of breakaway organizations. Those that can unlock the right combination of data and analytics to deliver tailored decision-making capabilities — at scale — will rise above the rest. On its face, it’s a straightforward formula: provide people with a BI tool that allows them to readily retrieve the data they need to generate insights.
Look at the mission or vision statement of just about any self-professed BI and analytics tool, and you’ll see remarkable similarities. “Help people see and understand data.” “Move faster, work smarter, and lead the way forward with an end-to-end solution for getting value out of data.” To the casual observer, all analytical tools provide the same value — they’re nothing more than a commodity. Simply pick the most well-known or hyped vendor, connect your data, and you’re off to the races.
“Across industries, we see organizations investing heavily to integrate analytics throughout their entire business in an effort to capture a portion of the $9.5 trillion to $15.4 trillion of value that the McKinsey Global Institute estimates advanced analytics can enable across industries globally.”
What's behind this myth?
Data strategies are in a constant state of flux. When organizations migrate data from an on-premises database to the cloud (AWS Redshift, Azure, GCP, Snowflake, etc.) to save on infrastructure costs, or when a vendor updates an application that changes a backend data structure, the downstream analytics models, visualizations, and dashboards are often the first to break. With typical BI tools, it’s common to invest significant time and resources manually rebuilding all the views.
While many data and analytics leaders prepare for these changes ahead of time, sometimes little thought is given to the true impacts on the business. Either way analytics professionals expect to commit significant time and resources. Why? Most traditional BI and analytics tools were never designed with flexibility in mind. They were designed for individuals or departments, not organization-wide needs. When big changes come, the impact on analytics and reporting is time-consuming and expensive.
“The first generation of BI tools was designed around the needs of corporate data warehouses, and they remain a vital part of the business intelligence landscape,” Forbes reported in March 2021. “But with the growth of unstructured data in multiple different sources... a human being can simply no longer grasp the amount of data that organizations manage.”
“53% of companies have an on-premises data warehouse and 36% have one in the cloud.
What's behind this myth?
Within many organizations, it’s common to see different people using different departmental analytics tools. While a bottom-up approach makes analytics and BI tools widespread, the transformational benefits are rarely seen. Tools require technical skills, resulting in backlogs of requests and inconsistent reporting. When organizations continually add new tools designed for specific people and use cases, it exacerbates the growing problem of tool sprawl.
What’s behind the adoption of so many different analytics tools is the belief that people within different parts of the business each have specialized needs and requirements. Departmental needs are unique both in terms of their data and reporting requirements and the access to the technical resources necessary to use the technology. As a result, adoption remains low across the organization and the goal of being “data-driven” continues to be elusive.
“Less than 20% of people who need access to BI/Analytics tools have access and the analytics experience is difficult to scale because tools are difficult to use.”
McKinsey & Co
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