Why you can’t keep up with requests for data science support

The volumes and sources of data are snowballing. To support this avalanche of data are a growing number of applications and tools for analytics and data science.

Because of these evolving capabilities, companies are three times more likely to report significant improvements in decision making when they use data. However, less than .5% of available data is able to be used in analytics. It’s an increasing challenge to keep up with company needs for data science support.

The challenge is real for IT, operations or strategy managers, and most companies are not equipped to use their data in an efficient way and build a data-driven culture.

The culprits: centralization, data saturation, snowballing of data, erroneous data, and talent gaps. These issues prevent managers from keeping up with the data science and analytics needs of their organization.

Centralized Data Science Team

Many companies centralize their data science team. As a result, the data science team becomes an isolated gatekeeper of data and insights.

Why Companies Centralize Their Data Science Teams

There are many reasons companies centralize their data science teams. Managers want to govern access to data. In other cases, managers think non-technical employees won’t have the skills or tools to work with the data.

In some companies, data privileges belong to the IT manager, the data team, and some executives with rigid data governance policies that exist. Frequently, this is in response to regulations like GDPR, CCPA, PPI, etc. and can’t be avoided.

The limited access to data throughout the organization increases the pressure on the data science or IT team to meet the increasing requests from personnel throughout the company. Business units and department heads are being asked to make data-driven decisions…but depend on IT or data managers like you to provide them with the information.

In the end, centralization can compound inherent obstacles you face trying to fulfill data science needs.

Why Your Current Data Science Team Can’t Keep Up

Data Saturation

More and more companies are collecting tons of data. They collect data about customers, data about prospects, and data about competitors. Each department collects data for specific purposes

  • CEOs use data to understand outcomes and determine new strategies.
  • Management teams use data for scorecards and performance measurement.
  • Sales organizations gather data about customers in the sales funnel to understand engagement and responsiveness.
  • Customer support teams gather information about calls and chats to identify key trends and drivers.
  • Accounting uses data for billing and fraud or transaction monitoring
  • Quality and customer insight teams use data to monitor and improve customer satisfaction.

The volumes of data needed to support these requirements can be overwhelming.

Snowballing Data
Data continues to grow and grow to accelerate toward a point of saturation. To you, keeping up may feel like pushing a rock up a steep hill.

You get a handle on the current analytics, then new data needs are requested of your team. This growing data comes at you unstructured. The unstructured nature of this data creates complexity of gathering and analyzing it. So, the need for data science support increases.

Faulty Data
Faulty data costs companies millions of dollars each year. The most common examples of bad data include:

  • Incomplete data
  • Duplicate data
  • Incorrect data
  • Inaccurate data
  • Inconsistent or unstructured data

You and your data science team serve all the departments of your company. You must create consistent reports, make proactive recommendations, and answer a variety of data-driven questions. But when you’re centralized, understaffed and can’t keep up with data science requests, you’re likely to fall behind or leave opportunities on the table.

Data Science Talent Lags
There aren’t enough data science professionals to execute the latest techniques and technologies. Your current team must learn new programs and tools to execute — and fast. If the existing data science team must learn and execute data analytics work, they can’t meet other demands.

The impact of falling behind

Not meeting data analytics demands can ultimately harm your company. When employees can’t get essential insights or reporting relevant to their job functions, the company fails to understand its data, iterate on its strategies, and grow.

When your company can’t keep up with the need for data science support, it hinders the power of different departments to solve problems. As a result, the IT and/or data science team become responsible to advise on a wide range of issues outside its purview.

IT, operations or strategy managers are expected to train existing staff and manage significant talent gaps, all while keeping up with their daily demands.

Chisel helps organizations like yours find top quality, vetted analytics or data science professionals, for permanent and temporary or project-based needs, to help manage data and produce the data analytics needed to push your organization forward.

That means managers get the support they need when they need it, with access to talent across a range of technologies, tools, and industries.

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