Neal News: It comes down to strategy

Neal News: It comes down to strategy

It comes down to strategy

Businesses are dealing with a rapidly changing world, even more so throughout this pandemic. Digital transformation isn’t achieved by flipping a switch. It requires resilient, well planned-out, and agile approach to achieve your business objective.

Check out these stories to learn a few ways you can improve your strategies >

silver keys on table

The 5 keys to a solid data strategy

STRATEGY  BY DAVID MCCLELLAN, DIRECTOR OF DIGITAL CONSULTING

Why do some companies succeed in their digital transformation while others flounder? It all comes down to the strategy. When you align your IT and business units with a data strategy, you map out a path where each team can deliver growth and outcomes to stay competitive.

This guide takes you through the 5 key elements of a solid data strategy, including how to target your business objectives, architecture selection, change management, and more.

measuring tape

DevOps is not “one size fits all”

DEVOPS by Edwin Webster, Associate Director of Edge and IoT

The best DevOps implementations are tailored, but that doesn’t mean you need to start from scratch. Design the best fit for your needs with a mix of these common and custom elements for DevOps

person standing on stage with blue lights

Which database is right for you?

DATA by Ravi Patel, Director of Technical Architecture and Development

Should you go with an Azure data service, or Snowflake? It depends on your data needs and use cases. This guide will help you compare the market leaders based on key factors and scenarios.

key building blocks for predictive reliability

How to build a foundation for predictive reliability

AI/ML by Zach Perkel, Director of Data Science and Applied AI

Using AI for predictive reliability, or predictive maintenance, is enticing – but don’t let the new shine fool you. There are 4 building blocks that are critical to building this solution successfully.

hand holding sea shells on beach

How a walk on the beach taught me to be a better data science manager

MANAGEMENT by Annie Mae Platter, Project Manager

What does a seashell have to do with data science? One of our PMs explains the structure and stages of data science and how it can look almost circular from the outside.

snowflake logo on blue background

Snowflake: A revolutionary data warehouse experience

DATA by Divyansh Sharma, Cloud and Data Engineer

What makes Snowflake different? This platform’s unique architecture and flexibility stand out from the crowd when it comes to capturing the benefits of a data warehouse.

team meeting

Effective Agile sprint cycles for data consulting projects

AGILE by Jason Doll, Associate Director of Project Management Operations

Data projects can be highly complex, with a ton of moving pieces and many unknowns. That’s why our consultants use Agile methodologies, like sprint cycles, to deliver value quickly.

Playbook for becoming a customer centric organization

SERIES   BY GREG GOMEZ, VP OF BUSINESS DEVELOPMENT

These 5-minute episodes dive into the ways you can put your customer data to work and use your CDP to increase customer lifetime value and deliver better experiences.

  • Episode 1: How to become a customer centric organization with data
  • Episode 2: What does it mean to know your customer (and how you can achieve it)
  • Episode 3: How AI and machine learning can be used with your CDP
  • Episode 4: How to deliver the right message to the right customer in the right channel
  • Episode 5: How machine learning and workflows help operationalize and personalize

In case you missed it…

Classify, comply, delete: There are three keys to a strong data retention policy. We have some best practices for each in this handy guide.

How to use Python for data engineering in Azure Data Factory: Want to use your Python code in ADF? We’ve got you. This tutorial will take you through the process step-by-step.

Inference at the edge: Thinking about moving your AI workloads to the edge? Consider these 4 things first.

A trip to the Lakehouse: Databricks recently announced the lakehouse architecture, enabling BI workloads on data lakes with SQL Analytics. Best of both worlds!

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