A data-driven business environment may sound cut and dry, but it can have a strong, positive impact on your bottom line. Today, we’ll share what it takes to create a data-driven culture, and the impacts businesses see from using data to lead the way.
This article is part of our series Selecting the Right Visualization Tool with Confidence. Other articles in the series include:
Data democratization is definitely a buzzword. Like digital transformation and moving to the cloud, it is ambiguous and can mean different things to different companies. But like many buzzwords, it contains a nugget of truth. Data democratization is truly a shift. When choosing a cloud business intelligence product, it is important that the vendor’s perspective aligns with your own. Otherwise you will end up with that familiar situation that the product does not quite “fit” with your organization.
Some years ago, I was part of a successful data warehouse initiative at a large financial services institution. Overall the project had gone very smoothly, and we were able to produce analytics that did a good job answering questions for the business. However, I became aware of an interesting pattern. We began to receive more and more requests to produce answers to questions, rather than the business users answering their own questions. Folks were willing to wait in the queue for days rather than attempt to answer their own questions with the tooling provided. It was just easier for them to ask the data team.
This experience illustrates the essence of data democratization. Although we had produced a good data warehouse, we had failed to create tooling that enabled everyone to participate in answering their own questions. Here is a deeper explanation from Forbes.
No matter how many slick marketing campaigns tell you their product makes data easy, working with data is hard. Quanthub describes the skyrocketing demand for data analysts, happening because they are the bridge between data and knowledge.
(P.S. The term “data science” is all the rage, and you may be tempted to go hire a data scientist. But data analysis is where most answers come from and is much more generally applicable).
The ideal state is that anyone in your business can answer their own questions. However, the current reality is that many users are not comfortable working with data and operate more like consumers of reports and analyses. But there is a major shift happening right now: more people can function as analysts, one reason being that new tools are providing new capabilities to support this.
Here are fundamental roles on a modern data team, to highlight the change in the data analyst role:
The major shift in the data analyst role is they are no longer part of the data team. And this has nothing to do with a title or formal responsibility. More often, they are part of business departments, and they happen to have an interest and ability to understand the data. You may have encountered the person on the marketing team, for example, that is a spreadsheet maven and always seems to have a spreadsheet available. Or the person on the finance team who produces charts and graphs in presentations that show just how the company is doing.
Many companies are embracing this analyst-first reality and looking for tools that can support this mode of operating.
The buzzword folks promote a vision where everyone is working with data. The reality is that the analysts have become the true workhorses, and are the bridge to everyone in your organization successfully using data.
Adopting tools and processes that fit this reality will be of most benefit to your company. From our research, many vendors do recognize this reality and take varying approaches to supporting the data analyst.
One benefit to a crowded BI market is that it forces innovation. Below are some various approaches to the analyst experience.
A downfall of earlier tools (and I am only talking a few years ago, not to mention decades ago) is they assumed all users knew SQL. Most analysts come from a background using spreadsheets to analyze data. Thus, converting the data warehouse tables into datasets that feel more like spreadsheets is going to enable more people to use the data.
Sigma Computing is a company that has embraced this concept. Not only do they separate the data set generation from the analysis, they are all in on spreadsheet analysis. In evaluating their product, I found this intuitive and right in line with an analyst-first approach.
Holistics also pushes the approach of providing modeling separate from analysis. This allows the more technical data team to define datasets, opening up the analysis to a broader group of people.
Panintelligence is an embed-first tool that follows a similar approach of defining a model that it then uses to drive GUI-based creation of visualizations.
Each of these uses the information in the model to generate queries that leverage the data warehouse infrastructure. Some tools actually process the queries on the BI tool’s infrastructure, which can be a data compliance risk. Be sure to ask.
Allowing people who do not know SQL an ability to generate queries (rather than write them directly) has been a long time coming. The idea is not new, but only in the last few years have vendors been able to do it well. Like bumpers in bowling, this approach allows more people to use the database without ending up in the gutter.
Trevor.io leads with their query builder functionality, and they say this is where most of their users spend their time.
Chartio had really begun pushing this with their Visual SQL feature, before their acquisition.
Although this is a great approach and an improvement over requiring all users to know SQL, there is often a tradeoff. The easier a tool makes this for the user, the less flexible it becomes in the types of queries and analyses that are available. When evaluating tools that take this approach, be sure to understand how well they support direct SQL access, and what the tipping point is where getting into SQL is necessary.
This is an earlier approach that requires analysts to know SQL to make queries to the database. This is still powerful, especially if the analysts in your organization do know SQL well. This approach allows for a lighter-weight tool that can be quicker to deploy and use. It does, however, come with another tradeoff in that metrics and analyses will become inconsistent over time.
Metabase is a tool we have enjoyed using. They do have some query builder type of features, but we found that we quickly jumped into SQL in most cases.
Redash is very lightweight and essentially just a visualization tool on top of a SQL database. For someone who knows SQL, Redash is easy to get into and start using.
We were a customer of Mode before moving to Chartio. Because Mode takes a direct SQL approach, it is well suited for larger data teams that need good collaboration among each other. But it is intimidating for analysts with a less technical background.
Preset actually takes the direct SQL to its logical conclusion, with SQL in everything–queries, metrics, filters, formatters, etc. It is very powerful, but obviously requires some SQL expertise.
When evaluating business intelligence tools, recognizing the approach they take to the analyst experience is crucial. No matter how good your data pipelines, data warehouse, and other pieces of your platform are, the analyst is going to make your data initiative successful. This article discussed how to understand the data analyst in the overall process, as well as various approaches vendors take to providing a good analyst experience.
This article is part of our series Selecting the Right Visualization Tool with Confidence. Other articles in the series include:
The risk of data breaches is huge, and is one of the main reasons companies are slow to adopt cloud computing. Facebook is in the news today for over half a billion profiles being leaked, including personal information! Google had to pay over $55,000,000 dollars in GDPR fines in 2020.
If the big companies cannot get it right, why take the risk at all? Companies that do not become a data-driven business lose out to the ones that do. There is no sustainable alternative. Forrester research shows that organizations with data-driven insights are 140% more likely to create sustainable competitive advantage, and take tremendous market share from traditional organizations.
In this article, my goal is to give you an overall understanding of what you should pay attention to and how to mitigate the risks. I am no lawyer, so you should make decisions based on advice from qualified legal, accounting, and security experts.
This gets into a larger strategic question of digital transformation and whether to use cloud computing or infrastructure at all. In spite of all the benefits of using cloud infrastructure and SaaS tools, such as lower total cost of ownership (TCO), agility, and scalability, perception of higher security risk has been an impediment to cloud adoption.
Ultimately, each organization has to make this decision on their own. Strategy in any risk-reward decision is greatly affected by how to mitigate or minimize the risks involved. Some organizations take a risk avoidance approach instead–which in my opinion outweighs the risk.
When it comes to using a cloud-based business intelligence tool, you will see much shorter onboarding time, and your maintenance costs are zero. So time to value is shorter, and if the tool’s pricing is in line with the market, TCO will be lower.
Data can be just as safe, if not safer, in an online tool than in a tool you manage internally. Seriously. In today’s information worker world, people connect into your network from home, coffee shops, mobile phones, etc. They are likely already connecting to your internal systems over a VPN connection, and to several SaaS products such as Salesforce or Jira.
The top reason for data breaches is old, unpatched systems. When the security community identifies vulnerabilities in operating systems or network devices, they share these vulnerabilities in lists known as Common Vulnerabilities and Exposures (CVEs). This allows everyone to act quickly and with maximum information to resolve the vulnerabilities.
However, there is a catch. IT departments must actively manage servers, operating systems, and networks to apply these updates, so that they are no longer vulnerable. Although every IT department claims they are following best practices, the number of security breaches due to unpatched systems objectively states otherwise. Now, compare that to a product company, where every bit of their livelihood depends on keeping their SaaS tool patched and up to date–the incentives and human nature state are in their favor compared to your company’s internal systems
The second reason for data breaches is social engineering–tricking people into using weak passwords, sharing too much information, or providing an opening. Recent years have seen an 9-fold increase in these types of attacks, because of how easy and effective they are. These can be simple or complicated, but the methods attackers use often take bits of information from various sources to triangulate on a successful attack. Again, everyone assumes they would not be taken in, but the security research says otherwise. Using a cloud tool vs an on-premises tool does not impact this risk one way or the other.
You will likely run into more arguments against using an online business intelligence tool. With each, play devil’s advocate until you get past the platitudes and really understand how much of a risk each one might be.
This is critical. How much of your data goes to the SaaS vendor’s servers? Two methods exist, with one carrying more risk than the other. In the first method, your raw data is brought into the vendors’ servers, where it is transformed or modeled into an analytical data model. In the second method, only aggregated data is brought into the vendors’ servers. The second is much lower risk, and most newer vendors take this approach.
This is a foundational architecture decision by the vendor, and not one they will be able to change for you. Often a vendor will tout this as an important feature–bring your data into our servers, and we can provide valuable data modeling to make your team more efficient (etc, etc). However, in the modern cloud data architecture, this is not a must-have feature. Tools like dbt are much more suited for this transformation, allowing your BI tool to focus on presenting the data, not transforming it.
A second question to ask about the location of your data is where the BI vendor’s servers are located. Most will be running on infrastructure provided by one of the big three cloud infrastructure providers (AWS, Azure, GCP), but not always. Each of these providers has regions globally. Depending on your industry, you may be required to guard against your data being “exported,” simply meaning that it cannot be transmitted or stored outside of your country. This leads to a line of questioning with the BI tool vendor about where their servers are located, and how they protect against data accidentally flowing through networks it should not.
When using a SaaS BI product, three technical vectors are the main consideration. When evaluating a tool, focusing on these three areas of security will be of most benefit:
You should ask to review these, and have a technology architect or security expert familiar with our business to call out any potential issues. Some of these will be technical in nature–encryption at rest, encryption in motion, etc. But the real focus of these is on people and policy. Things like password requirements; approvals; audits and reviews; and procedures and communications in case of breach.
Depending on your industry, you will have various regulations around data security. Some of the more widely recognized are HIPAA and GDPR. By using vendors who have participated in attestations or audits, you defer to experts and push much of the cost of risk mitigation onto the service provider. Here is how it works: consider a situation where a cloud vendor wants to process Personally Identifiable Information (PII) on behalf of its clients. Regulations state that these procedures must be audited. If each of the vendor’s clients must pass through an audit, that could mean hundreds of audits on the vendor, and each client must pay for their own audit. An attestation allows for the vendor to be audited a single time, and the auditor provides an attestation to each of the clients. This is efficient, cost effective, and is the standard in the audit world.
SOC-2 is widely recognized as the standard for SaaS vendors. Developed by the AICPA, it is a robust framework that ensures a minimum level of compliance around data security controls. You should ask about this, as well as any industry-specific regulations and attestations that the vendor may have in place. Most of the companies we work with are not large enterprises, so working with one of the Big 4 accounting firms does not make sense for them. Linford & Co is the leading provider of attestations for SaaS companies and is a group I trust with these kinds of needs.
An up and coming risk mitigation strategy is to automatically monitor risk exposure of your vendors. The risk network curated by Cyber GRX. Not every vendor you are considering will be a part of this network. But if they are, using the information to reduce your third-party risk is an easy way to gain more comfort in using a particular vendor.
In some cases, a hybrid approach between on-premises versus cloud is available. This is especially applicable in systems with a lot of moving components, such as a data platform. Datateer is designed for the security-conscious customer, with high levels of segregation to ensure there is no “cross-pollination” of data, and that data never leaves your control. This is not the mainstream approach, which is to have your data flow onto vendors’ servers. This makes things much easier for the vendor to process, but increases risk substantially.
One of the pillars in our stack is Prefect, which has pioneered this hybrid approach. This approach is more difficult for cloud-based BI tools to achieve. But as mentioned earlier in this article, if they have designed for it, they can prevent your raw data from flowing anywhere unnecessarily.
Part of this hybrid approach could mean hosting your own business intelligence tool on cloud infrastructure. This will guarantee that none of your data flows onto a cloud vendor’s systems, but it is quite a bit more maintenance. And it exposes you to the problem mentioned earlier of old, unpatched security vulnerabilities. Surprisingly, few options exist in this vein. Superset is a young but great option for internal analytics. And if you get going and realize managing your own solution is too much to take on, the project creators provide a commercially hosted option at Preset.
Should be a no-brainer, but often this is overlooked. You and the vendor you choose should both have a Cyber Liability Policy including Data Breach Coverage. Regardless of whether you use a cloud vendor or on-premises solution, regardless of how good the information security policies and attestations make things seem, breaches are likely to occur. It is almost common knowledge that data breaches are a “not if, but when” situation. That doesn’t absolve all of us from our due diligence, but it certainly calls for protecting against the situation.
The benefits of using cloud-based business intelligence tools outweigh the risks. With a focus on mitigating and minimizing the risk, you can enjoy those benefits while protecting your business from the downsides.
In this article, we talked about key risks to be aware of and ways to evaluate BI tools in light of those risks. We also discussed ways to mitigate and minimize the risk of trusting a third party with your data.
Many vendors pay attention to all this and can help you understand the security posture of their products. You can also take advantage of Datateer’s free strategy sessions to talk through these risks and help make decisions.
Ultimately, the benefits will outweigh the risks for most, including you!
This article is part of our series Selecting the Right Visualization Tool with Confidence. Other articles in the series include:
Evaluating business intelligence tools is exhausting.
First of all, there are a ton of them on the market. The high-profile acquisitions of Tableau, Looker, Chartio, and Qlik must be inspiring to entrepreneurs who want to have similar exits. The field is crowded. Even after years of discovering vendors and evaluating their products, we still discover new products regularly.
Producing a basic tool–a UI on top of a visualization library–must be fairly easy, proven by how many are on the market right now. But building a solid product and company requires more than that. And so many nuances exist that can stop a report developer or designer in their tracks, or cause workarounds. How could you ever cover all those situations in your evaluation?
Every vendor’s message starts to sound the same, and is really some variation of how easy their tool will make the whole effort. Although 80% of the effort in data analytics efforts goes to data engineering and modeling, some BI tool vendors will tell you all of that can somehow magically go away.
From my experience, the BI tool is the tip of the spear or the top of the iceberg. It provides the visual culmination of all the work and analysis a company has put into their data platform–the presentation layer intended to produce something clean and useful.
Really, more attention should be paid to getting the data, analysis, and metrics right. However, because the charts and visuals are what most users will interact with, the BI tool you choose is critical.
This article is the first in a series that will share our direct experience, the experience of our customers, and contributions from the community in the Chartio migration research project (here and here).
Below we lay out the overall approach we have developed to compare apples to apples. And in the coming weeks we will have articles diving even deeper into aspects of a BI tool evaluation we have found to be important:
In a crowded market you need some sort of way to compare things objectively. If you have ever had the experience of buying or renting a place to live, you have probably experienced house hunting fatigue. You look at so many homes, they all start to blur together. You can’t remember whether the kitchen you liked was a part of the first home you saw, or the second. Did you really like that 2nd house, or are you just tired when you see the 8th and want to make a decision?
That is the feeling we had when trying to make sure we were evaluating all the options.
To combat this many people make a list of features or other aspects of their evaluation, and then create a scoring or rating system. We started that way, too. But numbers in a spreadsheet only go so far.
Another thing we learned was this decision was not a one-time event. Each time we learned something new about any given tool, we found ourselves revisiting the spreadsheet to re-evaluate. We needed something that we could return to time and again, without wasting time rehashing things already discussed or re-orienting to a bunch of numbers.
We are evaluating a visual tool, so why not do something visual? By giving each feature two ratings–importance and score–we were able to create a simple visual experience.
Here is a sample.
At a glance, we can:
Check out our Product Evaluation Matrix. Feel free to make a copy to get your own analysis started.
Regardless of how you score or evaluate, each vendor is trying with all their might to be different from the others. So we end up with many features and variations of features, many of which are appealing.
But not everything can be of critical importance to your company. In a perfect world, you could enumerate the features you want, and someone would give you an order form and a price tag, and you are off to the races. But in an imperfect world like ours, you have to choose a tool that most closely matches your needs and desires.
It is the tendency of all us humans to overdo it and assume too many things are critical. At the extreme, if everything is equally weighted in your decision, you will end up with the most average product on the market, rather than the one that fits you best.
Buying decisions are emotional. Most of the decision is subconscious. According to the best research we have, emotion is what really drives the purchasing behaviors, and also, decision making in general. Experienced salespeople also know this, hence the saying, “sell the sizzle, not the steak.”
95% of thought, emotion, and learning occur in the unconscious mind–that is, without our awareness
Gerald Zaltman, How Customers Think
We use objectivity and logic to talk ourselves into things. Recognize this about yourself and the dynamics of the evaluation and decision process.
Immediately after a good demo, you will have an affinity to the product that might stick. This is why every product company is willing to spend a lot of time demonstrating their product. Or, if the sales engineer had a bad day, it might cause you to see the product in less of a light.
To solve this problem:
I highlight this point specifically because we made the wrong choice prior to discovering Chartio, and had to live with the consequences. We also found that a completely exhaustive review of every single aspect of every single product that might just be the right one is totally impossible. Maybe you have that kind of time, and if so then use it. But if you are like us, you need to make a decision on somewhat incomplete information.
To mitigate the risk of a bad decision:
The article by Barbara Wixom and Jeanne Ross was important to my learning about data strategy. They identified the three ways to monetize data.
When someone realizes they are sitting on data that could be an asset, it is like an explosion of ideas and possibilities. I love seeing an executive go through this transformation of thought. They see literally dozens of opportunities to create value using the data their company owns or has access to use.
The three ways to monetize data are:
If you are gathering a lot of data specific to your niche, there are probably people that want like to analyze it. The stock exchanges are a recognizable example. They have transactional information about buys, sells, options, etc. A single transaction is quite boring, but in aggregate they become powerful. This is proven by just how commonplace reporting on financial news is, and how ubiquitous stock price charts are.
Your niche is not as globally interesting as that, but it is probably even more interesting to your customers, partners, and industry analysts.
This is a natural place for branstorming to go, and an idea that comes up almost every time. It seems so easy on the surface. Unfortunately, it is the least accessible. It requires a completely different business model than what you are already in–including different buyers, different sales processes, and different support and delivery mechanisms.
Managers who have accurate metrics of their operations and a convenient way to evaluate what is actually happening on the ground make much better decisions than otherwise. They know what should be happening, and having data to indicate what is actually happening empowers them to effectively manage. Alternatively, they manage to the squeaky wheel, by what they can directly observe, or by gut feel.
It’s not hard to see how this could make things more efficient and reduce waste and costs. This is also the most familiar territory, and business intelligence and its host of products and vendors have made this commonplace.
This is a great place to start. The downside is that it isn’t directly monetizing data. It’s ROI is usually in incremental improvements to processes already in place. So there is a limit to the cost savings you could expect to see–only so much juice you can squeeze from that orange.
To me, this is the most exciting and most accessible way to monetize data. Companies that get this right have reported being able to close more deals, grow key accounts, and improve retention.
Customer-facing analytics can come in many forms. Providing a dashboard within your web app or web site is nice and straightforward. This can inform the customer about the use of your services, or even benchmark their use against the rest of your customers in aggregate.
You could produce simple reports for sales teams that enable them to have more meaningful conversations with their buyers and even get access to strategic roles in their target accounts.
One thing I caution against is immediately charging a fee for your first foray into customer-facing analytics. Although this is easy to stick in a spreadsheet and show how much money you’ll make, customers rarely respond by pulling out their credit card for a feature they didn’t ask for. A better strategy is to differentiate your core offerings and use that differentiation in the sales process.
I like that these three categories cover pretty much any monetization strategy you can come up with. You know your customers and you know your market. Any of these monetization strategies have been proven to create value. If you want to impact top-line revenue, start by adding customer-facing analytics into your core product. If you want to focus more on efficiency, start with internal analytics.
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