Sales Analytics: How do the new BI players ThoughtSpot & Hyper Anna compare to Power BI & Tableau ?
By: Hyper Anna
Tags: Bi, Analytics, Tableau, Sales, Power Bi

Part 3 — Power BI vs Tableau vs ThoughtSpot vs Hyper Anna

What are the key differences between dashboards (Power BI, Tableau), search-based BI (ThoughtSpot) and automated data stories (Hyper Anna)?

Strap yourself in. This is a long…

TABLEAU & POWER BI

Use-case: “I want a dashboard”

Sounds simple. And the use-case for a dashboard is simple — the ability to monitor high level KPIs. But although dashboards are widespread, it doesn’t mean that they are actually used by sales teams to get insight. In fact, the reality is bleak: the utilisation of traditional BI tools is incredibly low, between 17–30% (Source: BARC, Gartner).

Case in point — look at the sales dashboard below. Other than reporting on a few metrics, does this help answer burning questions? Shift the needle to get you close to closing a deal? Spark ideas or next steps?

And this is what your dashboard could look like: Charts and numbers. Zero insight.

The Hidden Costs of Power BI and Tableau

If you want a sales dashboard, here is what you’re really, implicitly, asking for:

I want a data analyst to build me a dashboard. As this relies on a specialist developer, I don’t mind waiting weeks or months for it to be built. I understand that I am not only paying for the software, but for the developer’s time to build the report. If this report isn’t a priority for the broader organisation, I understand that my request for insight will stay in a backlog, waiting to be prioritised. I know upfront what I want to see. The depth and breadth of insights that I’ll be able to see are completely predetermined, and I’m ok with this. My use-case is simply monitoring high-level KPIs, that’s it. I recognise that if I want to ask questions outside the scope of the dashboard’s filters, that I won’t be able to do this. I’ll need to rely on others to answer questions as they arise. Which means I’ll need more time and money. I recognise that the dashboard merely reports on some key numbers. But it’s not a tool that generates insight. As a leader, I recognise that if people in my team want to dig deeper for insights, they’ll fall into one of two buckets: 1) export data extracts into Excel and attempt to do the analysis themselves, or 2) they’ll make decisions blind, without insight addressing critical questions. In the latter case, I recognise that the team is likely overwhelmed: analysis is complex, time consuming, prone to errors, requires expertise, and requires training to know the right questions to ask of data — analytical skills and experience they lack, impacting their confidence or desire to get insights from scratch. They also recognise that asking data teams for insights is most often fruitless — get a ticket and wait to be prioritised in the backlog of data requests. I recognise that the quality, design and how long it takes for my dashboard to be built depends on the developer building it. But that fancy bells and whistles may also not be necessary, as my use-case of tracking high-level numbers is completely pre-determined. I recognise that rolling out a dashboard is a ‘safe choice’ as almost every company, every department, every manager has a use-case to monitor high-level KPIs, and access to Power BI via their Microsoft suite. But I’m aware that my team, account managers, are not going to get much from the report. Yes, it might serve as talking points during our team meeting, but because it lacks insight, it’s not going to help us think differently, speed up decision making or spark collaboration that will help us close deals faster.

THOUGHTSPOT

Use-case: “Like Google Search, I want to ask my data questions & get answers.”

Sounds simple. But here‘s what you’re really, implicitly, asking for:

I know how to think analytically, able to ask questions of data to get the depth and breadth required to generate insight. I recognise that using a question-answer model, relies on people knowing how to ask questions — how to phrase questions, how to ask follow up questions, and how to work through a business problem, pose hypotheses, compile answers, ask follow up questions, and arrive at a conclusion. I know how to ask questions to benchmark, compare and contrast results, so I can not merely present standalone facts or data points, but out context around results. I recognise that it’s not true natural language, but I have to learn to use syntax and keywords to get results back. I know how to think critically of analytical output. I recognise that getting an answer back — a chart — doesn’t make an insight, as it lacks necessarily supporting context required to trigger an a-ha! moment. I recognise that my output is biased, influenced by the situations when I’m triggered to ask questions and make the time to seek those answers. I also recognise that my analysis is biased by the questions that I know to ask, that insight may be further hidden within the data, but not surfaced. I recognise that I have to spend time ‘pinning’ answers to build a coherent data story, and that this will take time.

ThoughtSpot: Ask a question. Pin a chart.

HYPER ANNA

Use-case: “I need insights. So if there’s insight in my data, I want to know about it. Without waiting, relying on specialists or asking a question.“

Hyper Anna automates the process of analysing data, generating analytical content, compiling explanations (both visual and written) and encouraging collaboration (share, comment, subscribe). As such, Hyper Anna’s key USPs are:

#1 EASIEST TO USE & MASTER — Lowest learning curve

No code, no app to install solution. A real time, self-service, SaaS analytics solution. Cloud native and agnostic architecture. Hyper Anna is customer centric, empowering both data and business teams.

Built for business teams, Hyper Anna’s ease of consuming information is end-user experience focused.

For data teams, the learning curve to master Hyper Anna is two hours, a fraction compared to Power BI and Tableau. Data professionals stating that it takes four years to build the expertise to achieve insights in legacy tools comparable to the quality of insights generated in Hyper Anna.

Hyper Anna: No code, no app to install solution, with the lowest learning curve.

#2 LOWEST COST PER INSIGHT — Highest ROI

Hyper Anna delivers the highest ROI compared to all BI competitors due to the end-to-end automation in insights generation.

On average, a Hyper Anna user consumes 148 insights per month on the platform. With an average licence cost of $200 per user/per month, the average cost per personalised insight is $1.50.

Comparing this to the cost of achieving the same insight manually — $670 cost per insight (assuming 4 hours in resource time to generate one insight manually and following the national salary benchmark) — Hyper Anna significantly lowers the entry point for organisations to create and consume insights.

#3 BUILT FOR BOTH data analysts and business teams

For data teams, Hyper Anna handles 80% of insights requests, leaving the 20% to data experts. For business teams, engagement with Hyper Anna content is significantly higher and more sticky compared to traditional BI tools. C-suite leaders (CEOs, CFOs) and frontline teams (contact centres, customer service consultants, salespeople) use Hyper Anna every day. Whether Anna’s insights form part of a daily 15 minute stand-up or a more formal weekly meetings to track cadence, teams are engaged with data unlike ever before.

Hyper Anna: Smart on the inside…

#4 AUGMENTED ANALYTICS NATIVE — Smarter data analytics that rivals humans

As an augmented and contextual analytics native, Hyper Anna has focused on helping speed up analytic insights into operational actions from inception. Powered by machine learning, data contextualisation and optimisation delivers significant signals, not noise. The sweet spot is time series data. From data profiling, analysis, anomaly detection, and root cause analysis, Hyper Anna delivers smarter data analytics that rivals output produced by humans.