Shadow Business Intelligence Tools – The stakes in transforming into a data-driven business have never been greater. From the sales floor to the C-Suite, every department wants to use data to their advantage. 88% of executives say [1] they feel an urgency to invest in big data initiatives, and it makes sense. Companies that embrace analytics and business intelligence (A&BI) continually outperform those that do not. Data-driven companies are 23 times more likely to acquire customers [2].
The change to adopt the data has led to the recording of A& BI expenses. 55% of companies report investing $50 million or more in big data initiatives [1]. No matter how big your BI budget is, choosing the right software is crucial to your success. But let’s face it: the business intelligence software landscape is gigantic. With hundreds of vendors, knowing who to evaluate and what to look for in a solution can be overwhelming.
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If you are just starting to evaluate software, you may not be sure where to start. What’s the difference between all these BI software vendors? What capabilities and characteristics are important to have? How should the process be approached? What has changed since your last BI purchase?
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In the following pages we explore the top 7 questions every company should be asking during the assessment process and break down some of the top BI trends and features to consider along the way.
If you’re new to the world of business intelligence, or if you’re coming back after a hiatus, it’s helpful to have a solid understanding of where we’ve been and where we’re headed.
Historically, business intelligence has focused on tracking and curating data, usually in the form of dashboards or reports. Early BI tools were designed to show high-level insights to dashboards on a schedule, but lacked real-time reporting capabilities.
These dashboard-focused tools typically answer predetermined questions that executives have raised in advance. If the questions changed or additional information was needed to make decisions, the dashboards needed to be modified. Only technical people with knowledge of SQL or other coding languages could create them, which was handled by a member of the data or IT team, effectively making them the gatekeepers of the data.
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In the past, implementing a business intelligence solution meant building an on-premise data center and hiring an army of IT talent to manage it. Because data storage and computation were relatively expensive compared to today, data analysis was limited.
Outdated data was often removed to save massive storage costs, preventing long-term historical analysis. Computing and resource limitations, combined with the inability of business teams to dig into the data behind these dashboards and reports, limited ad hoc analysis to situations where it was absolutely necessary, leaving many questions unanswered. And without the ability to ask these questions on the fly, mission-critical insights remained hidden.
The data sphere looks very different today. The volume, variety and speed of data produced is unmatched. Thanks to the Internet, mobile devices and the Internet of Things (IoT), more data is created and collected than ever before. In the time it takes to walk to the water cooler and back, more than 3.8 million queries are sent to Google and nearly $1 million is spent online [3].
Data speed and volume show no signs of slowing down. IDC expects the global data sphere to exceed 175 zettabytes by 2025 [4]. The pace of change in data and customer needs in today’s business environment requires the right tools to keep up—tools that can provide all employees with real-time access to relevant data and insights.
Components Of The Business Intelligence Landscape
This new data economy is powered by the modern cloud data warehouse (CDW). Modern CDWs collect data from any source and scale elastically to support nearly infinite users and ad hoc analytic workloads. This includes support for unstructured and semi-structured data such as JSON. Storage and computation costs have also been greatly reduced, meaning that historical data does not have to be released and the technology can meet, and even exceed, the demand for information.
No wonder analysts expect 83% of enterprise analytics workloads to be cloud-based this year. But despite the vast amount of data available and many opportunities to use it to drive decisions, 73% of companies aren’t using it.
So where do you go from here? You need a BI tool built to thrive in the new data economy. But how do you know which one to choose? 7 Essential Questions for Evaluating BI Tools
If you’re comparing BI software, we’ve put together a list of questions to help guide your evaluation criteria and ensure you choose the best solution for your business needs.
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As you evaluate BI and analytics software solutions, be sure to look at cloud-based tools that take advantage of CDW’s capabilities. Things are changing faster than ever, and teams need access to real-time data to make sound, but fast, decisions.
Today’s volume and variety of data is much better managed in the cloud, not stuck in a slow local database or sitting in an extract on someone’s computer. That’s why 68% of the database market growth is in the cloud[6].
Unfortunately, many companies that have invested in CDW are still using BI tools designed to meet the needs of the pre-CDW era. These solutions fail to maximize the value of CDWs by requiring data extracts prior to parsing, which makes parsing semi-structured JSON data difficult and presents other obstacles that slow time to data insights.
Most analytics tools available today have some form of cloud offering, but few were built for cloud data storage. Look for a BI solution that gives teams direct access to data within the CDW.
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These modern BI solutions accelerate time to business insights by querying live data with your CDW and leveraging the computing power and speed of the cloud to quickly analyze massive data sets in real time. They also take advantage of cloud benefits such as elasticity, real-time data access, sharing, and usage-based pricing.
The desire to arm employees with knowledge leads 62% of companies to say that self-service business intelligence is essential in 2020
. Self-service BI tools are designed to give business experts (such as marketing VPs, sales operations managers, and product managers) the ability to find and analyze data without relying on IT professionals or business analysts. dedicated data to create reports.
But many “self-service” BI tools fall short of this promise because they require SQL or other proprietary coding knowledge, effectively restricting data access and analysis to technical analysts. This has left BI teams stuck in “Report Factory Hell” with constant ad hoc analysis to answer pressing business questions. Not only does this leave data experts unsatisfied, but it’s also a waste of their time and the company’s money. As more companies make data a bigger part of their culture and look to adopt a data-driven approach to decision-making, new tools are emerging that deliver on the promise of self-service analytics. These tools aim to put data in the hands of any employee and tend to have shorter learning curves, do not require manual SQL writing, and meet the needs of people without extensive data science or analytics training.
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Think about how you want people to interact with the data. Will they only see pre-built dashboards, or do you want domain experts in marketing, sales, finance and other departments to be able to ask follow-up questions and get insights directly from your analytics solution? If so, you’ll need a true self-service tool that allows business owners with no SQL background to visually explore and query data.
Look for tools that provide intuitive interfaces (such as a spreadsheet), visual analysis capabilities for the less technical, and SQL runners for those who prefer to code. These types of tools drive adoption and productivity because people with different backgrounds can interact with data and make informed decisions in real time.
Because Sigma looks like a spreadsheet, users haven’t hesitated to dive right into Snowflake data for faster insights.” – How Clover improved time to data by 90% with Sigma
Chances are your company is sitting on a lot of semi-structured data, such as JSON. JSON has become the preferred data exchange format for mobile devices, web applications, online services and sensors. This includes some of today’s most popular websites, such as Facebook and Google, and the rapidly growing market for wearable and IoT devices.
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These services and devices produce an unprecedented amount of data in our digital economy. Unstructured and semi-structured data now account for 80% of data collected by businesses. And that number is only expected to grow. This data is a potential treasure for companies that are able to leverage it effectively.
But reviewing JSON in real-time for patterns, emerging trends, and insights has historically been a challenge with BI tools. Extracting nested JSON rows and parsing them for insights still requires deep technical training, which means it’s usually off-limits to those outside the BI team. Even for those versed in SQL, the process can be time-consuming.
To get the most out of JSON, you need to parse its nested structure and parse the relevant fields. Look for BI software that makes it possible
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