Become a “Data Concierge,” Not a “Data Plumber”

Data Scientist

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One of the key takeaways from Gartner Data & Analytics Summit 2022, was that businesses should focus on becoming a “data concierge” instead of a “data plumber”. Gartner explained this by saying that instead of “setting up countless pipelines to bring data from its source”, we should “guide people to the right data.”

While this sounds great on paper, in reality, it is easier said than done. Several challenges have made it increasingly challenging for most businesses to derive the maximum value from their data at scale and in real time. Here, we discuss some of these challenges and how enterprises can stay on top of them with the help of the right mindset and by having the right tools at their disposal.

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87% of Data Projects Never Make it to the Production Stage.

But Why?

The typical data science project goes through 5 key stages: problem identification, data sourcing, processing, data modeling, evaluation, and production (also known as deployment). It is at the production stage that businesses start seeing the actual benefits in terms of ROI, scale, and dramatic improvements in the overall efficiency, effectiveness, and productivity of the business.

Alarmingly, 87% of data science projects fall apart before reaching the production stage, according to VentureBeat. Even for the ones that make it to final deployment, the odds of success just don’t stack up. According to Gartner, “Through 2022, only 20% of analytic insights will deliver business outcomes.”

But with the exponential surge in availability of data, and with the market being flooded with new tools for data analytics every other day, why is it that data continues to be an unresolved puzzle for organizations?

A big part of the answer lies in how the resources connected to data operations are being used and utilized. Broadly, three kinds of resources are required to mine the data for insights from start to finish – i.e. human resources, infrastructure resources, and financial resources. 

Human resources come in the form of data teams consisting of data engineers, data analysts, and data scientists. Infrastructure resources include the data management tools and technologies as well as the underlying architecture for data storage and retrieval. Finally, the financial resources include the money that is pumped in to obtain, process, and make the most of the data at hand.

In the case of each of these data resources, businesses are faced with critical challenges that severely limit their ability to transform data into the business bottom line.

1. Data Teams Have to Spend Lion’s Share of Their Time Doing Mundane, Mechanical Work.

Did you know? Data teams spend anywhere between 50-80% of their time collecting, preparing, and transforming data so that it becomes analytics-ready. This is because in data analytics, garbage in = garbage out. Without the data going through a rigorous process of cleansing, wrangling, and transforming, it won’t lead to any meaningful insights no matter how well crafted the data models are.

Hence, data experts waste much of their valuable time and expertise on building data views within data warehouses and making the same ready for visualization using downstream analytics tools like Tableau. Such an arrangement is equivalent to the popular adage of “architects laying bricks” and adds little value to businesses splurging money to maintain expensive data teams on their payroll.

2. The Vast Majority of Data Tools and Data Infrastructure Do Not Take a Unified Approach.

The biggest reason why data teams have to sweat it out is that most of the existing tools in the market do not take a unified approach. When it comes to integrating data from diverse and

disparate sources to glean meaningful insights from it, the existing data tools are like a set of scattered components that need to be manually orchestrated by the data scientists.

This also means that organizations have to invest in numerous data tools to harvest, process, explore, analyze, and visualize data from scratch. This results in broken or uneven data pipelines, challenges in maintenance, and data integration challenges. 

3. Costs Spin Out of Control Due to Delays and Quality Issues Across the Data Lifecycle.

Data systems are held back by two major shortcomings – high query response time and poor data quality. 

Even to diagnose the root causes behind any of the above issues, business users need to continuously seek intervention from the data team which causes back-and-forth communication, further delays, and rising costs. For example, poor data quality may be caused by inaccurate data, improper integration, inadequate data preparation, and so on. Similarly, high query response time may be caused by querying the wrong data set, a new query that isn’t properly supported, or a query that the system cannot understand.

If the data tools aren’t able to resolve these problems on their own in an automated and timely manner, then businesses lose money. This largely happens through increased man-hours, decreased productivity, and increased operational costs. In the end, a data science project ends up costing a fortune and generating little value – not something that the executives had signed up for.

Breaking the Vicious Cycle of Loss and Lag

Adopt a Unified, Automated, & No-code Data Analytics Solution

We at Verb Data are a frontrunner in powering no-code dashboard development at unprecedented speed while generating significant cost savings. Our product automates the entire data lifecycle without the need for any coding so that SaaS companies can focus on the core business while we manage all their data and dashboarding needs.

1. Automates Data Preparation: Verb single-handedly manages massive amounts of data that need to be cleaned, pre-processed, transformed, integrated, and analyzed at scale. There is no need for any manual intervention to generate custom reports and dashboards with just a few clicks.

2. A One-stop, All-in-one Solution: Verb does away with the need for multiple tools. Without Verb, businesses would have to use a different tool each time for different stages of data workflow like data sourcing, data integration, modeling, and custom visualization. With Verb, all those functions are bundled together in a single solution.

3. Drag-and-drop, Intuitive Interface: Verb provides a drag-and-drop interface for all data operations which makes it possible and easy even for non-engineers to model and visualize data. This empowers non-technical users and also saves time and effort for data teams.

To overcome the existing stumbling blocks in the data management ecosystem, the need of the hour is to opt for a product that drastically lessens the burden on businesses, their data teams, their IT infrastructure, and their budgets. Verb enables this with its no-code dashboard builder that dramatically cuts down on resources while increasing the results, outcomes, and impact.

If you want a tour of our newest features get in touch!