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Even small companies these days on average have 47.81 terabytes of data that they manage. Regardless if you’re a small company or a trillion-dollar behemoth, data is driving decisions. But as data ecosystems become more complex, having the right data strategy is the foundation to succeeding with data.
Developing a data strategy involves more than just considering what data warehouse or ETL tools you will be using. You will also need to think through the various use cases and business initatives your company is taking on.
But where do we start with your data strategy.
In this article, we have shared several articles your teams may enjoy that focus on developing and improving your data team's strategy.
1. The 5 Mistakes Ruining Your Data-Driven Strategy
Companies of all sizes have embraced using data to make decisions. However, according to a 2019 report from Goldman Sachs, it’s actually quite difficult for businesses to use data to build a sustainable competitive advantage.
Our team has worked with and for companies across industries. We’ve seen the good, the bad, and the ugly of data strategy. We’ve seen teams implement successful data lifecycles, dashboards, machine learning models, and metrics. We’ve also had to come in and untangle, delete, migrate, and upgrade entire data systems.
Throughout these projects, we’ve seen several issues that pop up repeatedly: alack of data governance; bad data; complex Excel documents; a lack of alignment between data teams and the businesses; and an over abundance of dashboards, leading to confused decisions.
All of these data issues compound over time and slowly erode a team or company’s ability to trust and use their data.
In this article, we’ll discuss some of these issues as well as possible solutions your teams can implement to improve your overall data lifecycle.
2. How To Modernize Your Data Architecture
Data is continuing to prove to be a valuable asset for businesses of all sizes.
I say that both from the fact that consulting firms like McKinsey have found that in their research companies that are using AI and analytics can attribute 20% of their earnings to it.
Similarly, I have been able to consult for several clients and help them find new revenue sources as well as cost reduction opportunities.
There is one catch.
You will need to develop some form of data infrastructure or update your current one to make sure you can fully harness all the benefits that the modern data world has to offer.
Just to clarify, I don’t mean you need to use the fanciest and most expensive data tooling. Sometimes I have steered clients to much simpler and most cost-effective solutions when it comes to data analytics tooling.
In this article, we will discuss what you should avoid when building your data architecture and which questions you should be asking yourselves as you try to build out your future data infrastructure.
3. 17 Questions You Need To Ask About Your Data Analytics Strategy
There are plenty of cliches about data and its likeness to oil or companies being data-driven. Is there truth to all this hype about data strategy, predictive modeling, data visualization, and machine learning?
In our experience, these cliches are true. In the past few years, we have already helped several small and medium-sized businesses take their data and develop new products, gain invaluable insights and create new opportunities for their businesses that they didn’t have before.
Many small and medium-sized businesses are starting to take advantage of the ease of access to cloud computing technologies such as AWS that allow your teams to perform data analysis easier and anywhere using the same technology billion-dollar corporations use at a fraction of the cost.
So what are you doing to improve your business data strategy today?
To help answer this question our team has put together a data strategy assessment that will help highlight where your team is doing well and where it can improve on its data strategy.
4. How To Improve Your Data Science Teams' Efficiency
Companies of all sizes are looking into implementing data science and machine learning into their products, strategies, and reporting.
However, as companies start managing data science teams, they quickly realize there are a lot of challenges and inefficiencies that said teams face.
Although it has been nearly a decade since the over-referenced data scientist is the sexiest job article there are still a lot of inefficiencies that slow data scientists down.
Data scientists still struggle to collaborate and communicate with their fellow peers across their departments. Also, the explosion of data sources inside companies has only made it more difficult to manage data governance. Finally, the lack of a coherent and agreed-upon process in some companies makes it difficult for teams to get on the same page.
All of these pain points can be fixed. There are tools and best practices that can help improve your data science teams' efficiencies. In this article, we will discuss these problems and how your team can approach them so you can optimize your data science team's output.
5. Developing A Data Analytics Strategy For Small Businesses And Start-ups
If you’re a small business or start-up, you’re probably reading articles about companies using data science, data analytics, and machine learning to increase their profits and reduce their costs. In fact, Mckinsey just came out with a study that found that the companies they survey could attribute 20% of their bottom line to AI implementations. All those trendy and hyped up words are proving to be effective for companies of all sizes.
As data consultants, we have had the opportunity to help multiple clients in industries like healthcare, insurance, and transportation realize similar gains and cost savings. All of which started with us helping them determine what was the best data strategy for them.
In this article, we wanted to take you through a few of the steps we walk clients through to help them figure out their future data strategy. We hope this article can help you take into consideration what your goals are and perhaps how data can help you achieve those goals in 2021.
How Will You Improve Your Data Strategy This Year?
Using data to make better decisions provides companies a competitive advantage. However, this depends on the quality of data and the robustness of data processes set up.
Simply creating dashboards, data warehouses and machine learning models is not sufficient to make data driven decisions.
Teams need to consider their data life-cycles and the processes used to manage each step. This means creating test cases, clear goals and processes can help improve your team’s performance and strategy. No one wants to get bogged down with too many processes and bureaucracy but having no form of plan or strategy for your team’s data life-cycle will also fail.
To avoid these problems, consider reading the articles above.
If you are interested in reading more about data science or data engineering, then read the articles below.
What Are ETLs and Why You Should Use Them
4 SQL Tips For Data Scientists
What Are The Benefits Of Cloud Data Warehousing And Why You Should Migrate
5 Great Libraries To Manage Big Data With Python
What Is A Data Warehouse And Why Use It
Hiring Data Science Guide – A Guide For Interviewing And Onboarding.
Kafka Vs RabbitMQ
SQL Best Practices — Designing An ETL Video
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