Why Hiring a Data Analyst Won’t Solve Your Business Problems

1 data analyst ≠ data-driven culture

By Gabi Steele, Leah Weiss & Barr Moses

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1. Lack of clear data ownership

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  • How many customer support agents do we need to hire to meet demand?
  • Which markets should we prioritize in Q3?

2. Lack of data democratization

Very often, the ability to work with and understand data is reserved for a select few in the organization, and despite the rise of data analytics training and data science boot camps, it doesn’t make sense for every data user in the company to become expert data modelers.

3. Your Data Infrastructure Doesn’t Meet Your Company’s Needs

One of the most common challenges companies face when investing in data is bridging the gap between data infrastructure and analytics. The modern data stack has enabled a new path forward when it comes to quickly setting up a strong technical infrastructure, but a lot of data infrastructure is inaccessible or inappropriate to a company’s use case, which means that investments of money and time never translate into anything that positively affects the bottom line.

  • Manual processes: Some solutions, particularly in the data governance space (think: data quality, data catalogs, and metadata management) still require manual input, a timely and cost-intensive process. If data governance and compliance is a priority for your company, data teams need a new approach that will keep pace with the growth of their business and reduce manual toil.
  • Involved onboarding and set up: As previously mentioned, most organizations need their data as soon as possible. Data teams can’t afford to spend weeks or even months onboarding or getting up to speed with a new solution.

How to build a data-driven culture

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Communicate value of data early and often — to everyone

The first question every company serious about data should ask is: how will investing in data help me solve our business problems?

Self-serve, automated tooling

Earlier, we mentioned that manual solutions and processes can make it harder to set started with data. While we’re far from the days of months-long onboarding and physical databases (you know, server farms and data centers in your office building), there is still room for improvement. Fortunately, more and more solutions providers are realizing the benefit of self-serve, cloud-friendly, and AI-enabled solutions to make tasks like data modeling, data exploration, and data discovery easier than ever before.

Data observability & reliability

It doesn’t matter how much time and energy you invest in your data infrastructure if you’re working with bad data, you don’t know where this data is coming from, or you can’t trust it’s up-to-date and accurate. An easy way to frame this problem is through the lens of software application reliability. For the past decade or so, software engineers have leveraged targeted solutions like New Relic and DataDog to ensure high application uptime (in other words, working, performant software) while keeping downtime (outages and laggy software) to a minimum.

Scale your impact by building a community around data

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We build data-driven organizations and create lasting data culture.

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