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.
–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.
–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.
Let’s face it. Every provider of Business Intelligence (BI) and Analytics would love nothing more than to see organisations consolidate and unify under their platform or brand. It’s the reality of standardisation.
The expectation is a seamless transition, but is that the reality?
Do all tools deliver the same results?
On the surface, it might seem so – in the end they all offer data visualisations. But delve deeper, and you’ll find vast disparities, from distinct authoring methods to chart originality. A tool that offers reporting cannot simply be replaced by one that doesn’t. For instance, substituting Cognos’s reporting functionalities with a desktop tool, particularly with data governance or external consumer requirements, isn’t just ill advised, it’s reckless.
It’s not just analytics vendors that are driving this narrative. Key decision-makers frequently opt to ‘switch’ tools when they procure a new one. The allure of reducing licences and associated costs is just too much for some, but like the sirens in the water, they often pull our attention away from the real danger. The cost of switching. Let’s break it down:
It’s great transitioning to a new tool with all its new bells and whistles, but you need to actually learn how to use the thing. Not only that, but you need to transfer any content from the existing system into the new one, placing a burden on the current content owners in terms of time and effort.
This becomes a people issue. Inform someone that you’re altering the tools and tasks they’re accountable for, and they’ll be inclined to shift to the new tool, or if that isn’t viable, they might choose to leave. If they leave, you risk delay and the loss of institutional knowledge.
The argument that existing staff will migrate the required content oversimplifies the business case. What about the new content and the aspirations of being data-driven that everyone is striving for? To demonstrate the value derived from this new expense promptly, you’ll need additional hands-on deck for these projects. More people equals more money. Money that you probably didn’t account for in the first place.
Operating multiple systems simultaneously will inflate hosting costs. We mustn’t forget the databases and source systems. Recall the challenges of conducting load and stress testing against production sources.
Losing users comes at a high price. This group is likely to voice the most objections. They may have advocated for improvements in the analytics experience, but standardisation implies a total change. During this period, this group is likely to fragment further. Rogue tools, new data export requests, or simply surrendering in the quest for the information they need can result in severe damage. This strays you further from the transparent and aligned data-driven culture you’re aiming for.
Keep in mind – Users who are vocal about their experience are the ones using it. Don’t mistake engagement and passion for bitching and moaning. You want engagement because it leads to enhancements and betterment across the board. Let’s not even talk about Outlook!
While you can try to minimise this expense, as the deployment of the new tools gets closer, users will need access to both. Validation and confidence building, as well as contingency planning if things go awry, are crucial for success (which is rare). It’s our firm belief that the BI and Analytics market figures floating around are inflated, as most users have multiple tools doing the same job.
Choosing not to invest in people to do the work equates to prolonged timelines. All vendors advocate the ‘time to value’ concept, but this is only achievable with simplistic projects and some “Services” to assist or train along the way.
So, what do we truly gain when we switch from one tool to another?
Recreating content that is already accepted and available. (There are no migration tools available between vendors only services groups.)
Training, duplication of resources (human and financial)
Forfeiting chances to create new content for new projects and analytics to effect better changes.
In this context, a SWITCH implies a:
If you’re contemplating a switch, here’s some advice:
Rearticulate the desired outcomes.
Reimagine the problem without the solution.
Reassess how the solution will meet the outcomes and tackle the problems, equipped with the insights above to determine if the cost is justified.
In our opinion, the only solid argument for switching is if the current solution is no longer vendor-supported.
Your Mind is Set on Consolidation
If you’re still keen on consolidation, bear in mind the inevitable truth. There’s no one-size-fits-all “ring”. While BI platforms might be replaceable at a high cost, they’re no longer the only sources for analytics. Every solution provider has a strategy to introduce or enhance the analytics provided on their platform. Often, these aren’t accessible to other tools or necessitate manual Extract, Transform and Load (ETL) of data.
To put it another way, what does consolidation or standardisation truly mean for your business? Could a Unification strategy be a more fitting approach? Let’s consider some typical outcomes:
Single place for analytics
FAIL External applications exist
FAIL Spend happens elsewhere as teams retool (shadow Ops).
PASS Reduction of ‘legacy’ tooling content happens over time by users naturally as does the expense.
FAIL Disruption, forced into a single tool, change is hard.
PASS Best tool for a job survives, minor changes, natural transitions.
FAILOne tool can’t do it all, multiple places still exist.
PASS Users are unified to access analytics; Uses are exposed to more analytics and use cases.
This should serve as an awakening for many data and analytics team owners (including executives). Consolidation has been a catastrophe, benefiting only service teams and vendors for the past 15+ years.
Some vendors were wise enough to see the value of a side-by-side approach, but the collective end users were overlooked, leading to negative outcomes.
There is a Harmony to All This
To put it simply, we think that Unification and harmony is the strategy with the most tangible benefits at the user level. It’s a strategy that’s already being implemented with considerable expenditure at the data layer with virtualisation, data catalogues and metrics stores (but is likely to fail due to the lack of end-user consideration).
If you’re operating in an environment with multiple Data and Analytics tools (as everyone is), you owe it to yourself, your organisation, and your staff to explore unification and harmonisation.
*Sorry readers, we used a bad word. There may be more throughout this article, but if it encourages you to critically examine the concept of standardisation and its negative impact, then it’s worth it. Sorry, not sorry.*
Yeah, you heard us right the first time. Standardisation in analytics tools, often hailed as the cornerstone of making technological progress, is bullsh*t. Don’t believe us? Let’s put it another way. Clinging to a single standardised analytics and bi tool can stifle creativity, hinder flexibility, and ultimately slow down progress within your business.
But that’s just one side of the argument. In this article, we’ll dive into the controversial statement, shedding light on both the pros and cons of BI standardisation, and challenging the widely held belief that standardisation is always beneficial.
The double-edged sword of standardisation
Standardisation is the process of creating and implementing technical standards based on the consensus of different parties that include firms, users, interest groups, standards organisations, and governments. It’s supposed to help maximise compatibility, interoperability, safety, repeatability, and quality.
It’s often associated with a number of benefits, including:
Increased efficiency: By creating a common framework for businesses to operate within they’re able to reduce costs and increase efficiency.
Improved quality: By ensuring that services meet certain standards they can improve the quality of those products and services.
Increased safety: As above but replace quality standards for safety.
Increased compatibility: Standardisation can help to increase compatibility between products and services, making it easier for businesses to work together.
Reduced spend: Removing expensive and overlapping tooling licences and removing the need for multiple ABI teams (one per tool) and overlapping infrastructure (per tool). Don’t forget better rates that come with increasing buying power with the chosen Analytics and BI vendor!
Sounds incredible, right? Who wouldn’t jump at the chance to be more efficient and cost effective while improving quality and safety for products and services. But as the subtitle suggests, this is a double-edged sword, and sadly this blade is pretty sharp.
You see, standardisation can also backfire for companies who embrace it. For example, it can:
Lead to vendor lock-in: When a company standardises a particular Analytics and BI platform, it becomes more dependent on that vendor. Making it more difficult and expensive to switch to a different platform in the future.
Reduce flexibility: Standardisation can reduce the flexibility of an analytics team. This is because a single vendor will do some things well and others not so well. The things it doesn’t do well lead to rigid solutions around the limitations.
Stifle innovation: Standardisation can stifle innovation by discouraging developers from developing new and unique analytics applications. This is because developers may be reluctant to invest time and effort in developing applications that are not compatible with standardised platforms.
Opportunity cost: Migrating existing content from other Analytics and BI tools is time consuming and costly. Typically, everything gets moved (without knowing the value), which means new projects aren’t being done and you’re missing opportunities wasting time on stuff that isn’t needed.
Not so great now, is it?
Standardisation Simplifies Interoperability
Another reason that people gravitate toward analytics standardisation is due to the way it simplifies interoperability or removes the need for interoperation entirely. The allure of everything working together seamlessly, fostering compatibility, and reducing friction in user experience is too enticing to miss out on. I mean, imagine a world where every manufacturer had a different design for electric sockets or USB ports – chaos would ensue – we’re looking at you Apple!
But does that mean you have to succumb to the other negatives we discussed? There may be a better way.
At Digital Hive, we like companies to have freedom within their analytics tech stack, utilising tools and services that tick every box based on need, not just a few because the others won’t play nicely together.
By layering Digital Hive over your analytics tech stack, you get the benefits of standardisation without the negatives that accompany it. Instead, you get to keep the ABI tools and services that work for your organisation and your individual business unit needs, while adding in a branded front end that is as simple or in depth as you need it to be.
Imagine a place where all your analytics assets live, easily accessible without having to reinvent the wheel on how it’s accessed. Now imagine having to standardise that content to fit a new product just because it plays nice with the flavour of the week tech that no one wants, but it’s part of the package you just bought. Got to get your money’s worth, right?
Stop Fitting Square Pegs in Round Holes
Okay, the title is a bit provocative, but you get the point. While standardisation offers undeniable benefits, it’s not a panacea. It can, and does, block innovation, reduce flexibility, and stifle competition.
The key is to strike a balance. By using Digital Hive to collate ABI software into one easily accessible front end, you can begin fostering an environment that encourages usage, improves productivity of users and power users, adapts to change and helps BI teams prioritize work and understand value. We can enjoy the benefits of standardisation without falling into its potential pitfalls. After all, in the dynamic world of technology, adaptability, speed and balance are the keys to success.
Thus, it’s not that standardisation that is bullsh*t; rather, it’s that blind adherence to standardisation, without considering its potential drawbacks and the need for balance, can lead us down a problematic path. By recognising this, we can navigate the complex landscape of technology with a more nuanced understanding and a greater potential for progress.
For more information about Digital Hive and how we can work with you to achieve amazing results, contact us today.