The analytics realm is evolving at a breathtaking pace, regardless of where you turn there’s a product offering analytics capabilities for your business, and for good reason. To make smart business decisions, you need robust analytics data. However, amidst a sea of numbers, graphs, and KPIs, one aspect stands out as the game-changer – User Experience (UX). As businesses across industries grow more data-driven, the tools they use become crucial. Yet, it’s not just about what the tools can do; it’s about how they make users feel.
The User Experience Imperative:
UX goes beyond aesthetics. In the world of Business Intelligence (BI) and analytics, UX encompasses how intuitively data can be read, how smoothly functionalities can be navigated, and how easily insights can be derived.
Efficiency, accuracy, and engagement are the three pillars of UX we, at Digital hive, find ourselves always coming back to. It’s the questions we ask ourselves when reviewing our updates to ensure that we’re providing not only the technology to improve the way our customers do business, but also the experience they have when they do it.
Here’s how we break it down:
Efficiency: A good UX design reduces the learning curve. It means our users spend less time trying to figure out how to use our platform and more time actually using it for analysis.
Accuracy: An intuitive interface minimizes errors. When buttons, toggles, and filters are clear and well-placed, the chances of misreading or making incorrect inputs decrease.
Engagement: A visually pleasing and user-friendly interface keeps users engaged. This ensures consistent use and higher ROI for analytics tools.
Differentiating in a Crowded Marketplace:
Consider two analytics tools – both offer similar features, data visualization capabilities, and integrations. Yet, Tool A has a cluttered interface, a confusing layout, and lacks clear instructions. In contrast, Tool B offers a clean layout, step-by-step guides, and an intuitive dashboard. Even if Tool A had a slightly better processing capability, users would gravitate towards Tool B due to its superior UX.
This differentiation is paramount in today’s crowded BI marketplace. With myriad tools boasting advanced functionalities, it’s the user experience that can set a platform apart.
The Role of Feedback in UX Evolution:
Continuous evolution is a hallmark of great UX design. Companies leading the BI sector, like Tableau or Looker, frequently update their interface based on user feedback. This iterative process ensures that the tool remains aligned with users’ changing needs and preferences.
Beyond the Tool – The Ecosystem’s UX:
It’s essential to remember that UX doesn’t end with the tool interface. Ask yourselves these questions:
How easily can the tool be integrated into existing systems?
How smooth is the data migration process?
These are also crucial UX elements. Solutions like Digital Hive, which seamlessly bring together various BI tools, exemplify the importance of considering UX in the broader ecosystem.
As analytics becomes an indispensable part of business strategy, ensuring an impeccable user experience is not just desirable – it’s imperative. In the vast ocean of analytics tools, it’s the lighthouse of intuitive UX that will guide users to your shores.
Companies that prioritize and continuously refine their user experience are the ones that will lead the charge in the analytics revolution, turning raw data into actionable, impactful insights.
Are we experiencing the Modern Business Intelligence Renaissance?
The last decade has seen the Business Intelligence (BI) space transform from a niche specialty into a cornerstone of modern business operations. With the data revolution, companies recognized the need for detailed insights to steer strategies. This need gave rise to a myriad of BI tools, each promising to unlock the secrets hidden within vast data lakes.
Whether you’re a data scientist, marketing professional, or bored (board) level tinkerer, there seems to be a BI tool to meet your needs.
The Titans of BI: More Than Just Tools
To truly appreciate the need for a unified approach, we probably need to understand why a company would choose to have more than one BI tool. Surely they all do the same thing, right? Spoiler: They don’t and don’t call me Shirley.
Here’s a quick breakdown of the most popular BI tools and their uses:
Tableau: Beyond its striking visualizations, Tableau champions a user-friendly experience. With drag-and-drop functionality, even those less acquainted with analytics can uncover patterns, offering a democratized approach to data-driven insights.
Power BI: While integration with the Microsoft suite is a key strength, Power BI also brings advanced data modeling capabilities. The tool’s DAX (Data Analysis Expressions) language allows for sophisticated calculations, making it a powerful tool for those with a deeper understanding of analytics.
Looker: Not just a visualization tool, Looker excels in its data modeling layer. It offers a unique perspective by turning database queries into reusable code chunks, enhancing efficiency and consistency across the board. It also benefits by having the backing of a tech giant like Google.
Qlik: Beyond its associative data modeling, Qlik’s in-memory data processing delivers rapid-fire analytics results, catering to businesses needing real-time insights.
The Fragmentation Problem: A Blessing and a Curse
While choice is beneficial, too much of it can lead to that old classic, operational inefficiencies. If your organisation features multiple departments using different tools then I’m afraid to say that you may have come down with one of the following affiliations:
Siliosis (Operational Silos): Where one department’s insights remain inaccessible or incoherent to another due to the BI tool they’re using.
Challenge-itis (Training Challenges): Onboarding new employees becomes challenging when they must familiarize themselves with multiple BI tools.
The Wallet Flu (Financial Overheads): Managing licences and updates for multiple tools can become a logistical and financial burden.
Unified Analytics: The Meta-Layer Revolution
Unified analytics platforms, like Digital Hive, are not about replacing these BI tools but embracing them. Digital Hive serves as an overlay, ensuring the diverse BI tools communicate effectively. Think of it as a mediator that features some great real world applications and advantages, like:
Streamlined Reporting: Imagine a global enterprise where the European arm uses Looker due to legacy reasons, while the North American arm swears by Tableau. Unified analytics allow for a consolidated dashboard that executives can use to gauge global performance metrics without delving into the specifics of each tool.
Leveraging Strengths: A unified platform recognizes that every BI tool has its strengths. For instance, data from Qlik can be combined with visualizations from Tableau to create a report that leverages the strengths of both tools.
Cross-Tool Collaboration: Consider a situation where an organization is working on a multi-departmental project involving both finance and marketing insights. Even if these departments use different BI tools, a unified analytics platform ensures they can collaborate without friction.
Beyond Mere Integration: The Future Vision of Unified Analytics
Unified analytics platforms are not just about integrating various tools. They envision a future where BI is seamless, efficient, and holistic, across an organisation.By bridging the gaps, these platforms are not only resolving the current challenges but are also future-proofing businesses against the evolving BI landscape.
We won’t sugar coat it. Navigating the complex world of Business Intelligence tools is no small feat. However, as we stand at the cusp of a new era in BI, it’s evident that the future is not about individual tools but how effectively they can be integrated. Unified analytics platforms are leading this change, ensuring that businesses remain agile, informed, and ready for the challenges of tomorrow.
It’s no secret that the volume of data being generated in 2023 is absolutely staggering. With this deluge of data, analytics tools have sprouted like mushrooms after a heavy rain, each promising to provide the most actionable insights to take your business to the next level. While some do, we’re looking at you Bundle, it’s probably a little surprising to hear that many companies have as many as five analytics tools in their business all working to provide these actionable insights.
This is where the main challenge presents itself. How do you manage these analytics tools without wasting time, money, and resources? Let us welcome you to the era of unified analytics.
The Fragmented State of Today’s Analytics Landscape
With so many analytics and BI tools on the market, companies often find themselves grappling with a jigsaw puzzle, attempting to piece together different tools for a cohesive view. While each tool might excel in its niche, the lack of interoperability often results in disjointed insights and a frustrated user experience.
The Power of Unified Analytics
Unified analytics platforms, like Digital Hive, address this fragmentation, serving as an integrative layer, bringing together various tools for seamless interaction. But who cares, we hear someone in the back of the room cry? You should. Here’s why:
Integrated Insights: Data sources are no longer siloed. They speak to each other, leading to richer, more holistic insights.
Optimized Costs: By unifying analytics tools, businesses can reduce overlapping tooling licences and benefit from economies of scale.
Enhanced User Experience: Users no longer need to hop between different tools. A centralized platform ensures a consistent and intuitive user journey.
Why Unified Platforms Represent the Future
Besides the benefits we’ve already mentioned, there’s a reason why more and more companies are speaking to Digital Hive about unifying their platforms. These are:
Interoperability: Unified platforms prioritize compatibility, ensuring different analytics tools can communicate and share data effectively.
Adaptability: With the rapid evolution of BI tools, platforms that offer easy integrations and can swiftly adapt to new tools are primed for future success.
Data-driven Culture: As businesses strive for a more data-driven approach, having a unified analytics platform fosters a culture of informed decision-making.
What are you waiting for?
There are 328.77 million terabytes of data created each day, and with those kinds of numbers it’s easy to see why businesses are drowning in data and disjointed insights. But with a unified analytics platform, like Digital Hive, businesses can now navigate these waters with clarity and purpose. By bridging the gaps between different analytics tools, we are not just optimizing our BI processes but paving the way for the future of analytics.
Analytics Catalogs have been listed as a Gartner Critical Capability since the 2022 version of their Critical Capabilities for Analytics and Business Intelligence Platforms. The goal of an Analytics Catalog is to unify and centralize all of a company’s analytics. There are many benefits to using this up-and-coming technology and here are the Top 5 as rated by Analytics Catalog users.
Centralizes the analytics experience for happier users.
Companies average 4 BI tools or sources of analytics. This makes it hard for users to find the information they need to make data-driven decisions. An analytics catalog allows for content to be organized for consumer consumption by job function, area, or role into a single-entry point. Users are provided with search and favoriting of all analytics from differing platforms making easier it to do their jobs. Easy to access analytics means quicker decisions based on data and more confident decisions.
Makes changing BI vendors easier by insulating users from change.
A new Analytics / Business Intelligence tool is created every day. Technologies change and the tools do too, but users are the ones that suffer. With an Analytics Catalog, users can continue to go to the same location and get the same governed content while technology teams change the underlying technology. We all know change is hard but with this technology we can avoid big bang changes and replace the regularly used content from the outgoing system piece by piece as the replacement system comes online.
Gives Analytics / BI teams and executives real usage details.
Some BI vendors make tracking analytics usage really difficult and when you have multiple BI tools, bringing that together is a “big data” project. Having a single point of entry to all your analytics via an analytics catalog creates a single source of audit data for analytics consumption regardless of the underlying BI vendor. Usage can be tracked by user, by BI platform, source and by asset just to list a few ways you can look at the data.
Usage can also come in the form of feedback. With a good Analytics Catalog solution, commenting and ranking is also available to help teams pure or improve the content they provide.
Increases analytics adoption and literacy.
Adoption starts with engaging the consumer. When consumers have multiple places to go, multiple experiences (some tailored and some not) it makes it hard. The analytics catalog creates a single place and experience for the consumers. A really good analytics catalog will let you provide different experiences for different groups of consumers. Data literacy really comes down to – does the user understand the data and the context they are consuming. There are so many fancy chart types, but you’ll find that the most used are the simplest (bar, column, pie) because they are easy to understand by the widest audience. To get to those fancier and sometimes better charts we need to explain and teach what they are showing. Lastly the ability to add context in the form of commentary is also helpful for consumers. Analytics catalogs here help by allowing grouping of analytics content together that is related. Could be a certain topic or related to a workflow. A great analytics catalog can put these assets or pieces of them together and allow for the create to add the text for explanation of what and why.
Provides pathways for Analytics Governance across the organization.
Analytics Governance is different from data governance. Data governance is rarely enforceable once in a BI tool. Analytics governance provides oversight of analytics assets, their creation, modification, and management. The data powering an asset can be governed by does not eliminate the possibility of someone hiding key elements or creating their own view by adding calculations or filters.
A good analytics catalog will allow users to differentiate content that is certified from not certified. A better analytics catalog will allow users to assist with the analytics governance by allowing them to provide feedback that can be tied to the usage data collected by the platform.
We love Analytics Catalogs and truly believe that any organization with multiple tools BI tools needs to jump on this technology for its users and to get the most of their existing and future analytics investments.
Three technologies that are being talked about but mistakenly intertwined and overlapped.
1. Data Catalog:
What it is: A data catalog is a centralized repository that contains metadata about data assets within an organization. It serves as a comprehensive inventory of available data sources, datasets, databases, tables, files, and other data-related resources. The catalog provides information such as data descriptions, data lineage, data quality, usage statistics, business terms and access permissions. The primary purpose of a data catalog is to enable data discovery, facilitate data governance, and improve data collaboration across teams. Content producers (e.g.: Data Analysts and Data scientists) are the primary consumers for this service.
What it is not: A repository for all things upstream like Power BI files, Tableau Workbooks, Notebooks or report and dashboard definitions. All the data used in semantic layers, business definitions and other analytical artifacts should have lineage traceable via a Data Catalog. An exception to this is where data products are produced from other source data, in these cases that definition is required to trace lineage back fully.
2. Metrics Store:
What it is: A metrics store is a specialized storage system designed to be an additional, intermediate area between the data source (database, warehouse, file) and other upstream systems, esp. BI/analytics solutions. These repositories contain definitions of the underlying data and form a semantic or business layer to promote content users to use common ways of using, accessing, and manipulating (e.g.: calculations and normalizations). Content producers are the primary consumers for this service.
What it is not: A repository for data or analytics assets. Its job is to make upstream reports, dashboards, and visualization creation easier with reusable business and calculation definitions.
3. Analytics Catalog:
What it is: An analytics catalog contains the metadata associated with analytical assets and artifacts. It provides a centralized repository for storing and organizing, analytical reports, dashboards, visualizations, and other analytics-related objects from various locations and vendors. The analytics catalog helps data analysts, data scientists, business users and all consumers discover and access analytical assets, understand their context and business logic, and promote collaboration and reuse of analytical work within the organization. It also helps Analytics and BI teams get a better understanding of usage of usability to help focus their efforts.
What it is not: It is not another Business Intelligence tool. It does not require access to data or replication of data. It is not a technology used to define metrics outside of other analytics systems in use.
In summary:
–Data Catalog: Contains metadata about data assets (datasets, databases, files) to facilitate data discovery and data governance.
–Metrics Store: Contains business ready definitions of data to facilitate data consumption in analytics tools.
–Analytics Catalog: Focuses on metadata related to analytical assets, reports, and dashboards, to support analytics collaboration and reuse.
While there may be some overlap in functionalities (like they all have search and they all live in the world of Analytics), these three components serve different purposes and cater to different aspects of data management and analytics within an organization.
