Why You Need a Data Strategy
Stop treating data as a byproduct - start being strategic.
Your company has a lot of data, but how do you manage it? Are you getting insights when you need them? Who at your organization is supporting data? Do you know how to collaborate using data, or is it siloed? Is your process efficient?
At Toric, we have an opportunity to speak to many data analysts and business leaders. We've found that many organizations do not have cohesive data strategies. Many data pipelines and inquires are created to address specific tactical issues or are insufficient in providing them the insights they require.
This treatment of data as an afterthought is common. But barriers that previously existed are being knocked down with new technology such as no-code.
Let’s go over what a data strategy is, common barriers to data strategies, and how no-code tools like Toric can help support a healthy data strategy.
What Is a Data Strategy?
a central, integrated concept that articulates how data will enable and inspire business strategy."
A data strategy aligns your business plan and priorities with your technology strategy. It’s a long-term, forward-looking approach and plan of any organization or business with the fundamental goal of achieving a sustainable advantage by facilitating data-driven decisions with data access, collaboration, and use.
Why Do You Need a Data Strategy?
A data strategy provides the basis for all business’s planning efforts connected to data-related capability. A data strategy enables you to wake up dormant data and leverage your data for actionable insights. Instead of wasting valuable efforts on creating systems to maintain your data, teams are empowered to find actionable data insights. These data insights which are acted upon make organizations more efficient and thus more competitive.
A data strategy aligns your business plan and priorities with your technology strategy. It’s a long-term, forward-looking approach for any organization or business with the fundamental goal of achieving a sustainable advantage by facilitating data-driven decisions to optimize organizational efficiency with increased data access, collaboration, more use. deliver more work faster and with better outcomes for the business if you are driven by data – and facts.
5 Reasons Why Data Strategies Fail
1. Lack of business alignment & leadership buy-in.
A data strategy must align to business strategy or help enable business objectives. While leadership may be aware of the need for change and may desire a cohesive data strategy, the process can be such a massive undertaking that buy-in gets lost.
The complications come in from utilizing multiple complicated programs that each need their own strategy to implement. Each tool requires specialized knowledge, and reinforcement to sustain the change. Many nuanced issues arise, and it can be challenging to make changes- especially when dealing with multiple contracts to maintain all of the programs that make up the data pipeline.
2. The data team is inundated in maintaining the inherited data stack.
The personnel responsible for the data strategy is inundated with running the programs and re-running them rather than looking forward to their data strategy.
Often data analysis or engineers inherit a data pipeline process. Then they add to a data stack rather than re-evaluate the data strategy as a whole. Every component of the data pipeline requires a great amount of research and buy-in from various stakeholders.
So instead, when a pipeline and process are in place the team continues to run the same systems because buy-in is a hassle and they are tasked with completing specific projects instead.
3. Difficult to manage and oversee all parts of the data pipeline.
One of the reasons the pipeline is difficult to oversee and lacks management buying is that the tools are over-complicated. They require specialized coding knowledge as well as extensive onboarding and testing.
Specialized personnel such as data analysts or data engineers are hired to set up and maintain data pipelines created to answer very specific questions. These conventional data pipelines require multiple programs to run a single inquiry for data insight.
These programs make up components of the data pipeline and are typically unavailable to be monitored in a central location. Due to this, the individual components require constant checks to ensure the data is maintained properly, in the correct format, and is accurate.
4. Lack of data ownership.
Data ownership can get complicated. After all, what does it mean? Does a data owner own the data pipeline process, own an application, or are they a central controller? Who maintains the quality of the data? Who knows what questions to ask for insights? Who is responsible for data governance?
These are complicated questions, especially when the knowledge of process, techniques, and tools are required at every step.
Due to the specialized skills required, commonly, the person creating or collecting the data is not the person analyzing the data. Data maintenance and analysis is something that somebody else does for an individual that needs a specific data insight. Sometimes this is a data analyst or simply another team member putting together spreadsheets of data.
Data is collected ad hoc.
Data is collected in a multitude of ways as needed by different departments. There is not one overseer of this data to ensure it is collected in a similar format or available for data exploration across departments and data sets.
Different departments and individuals will solve data issues on their own if there is no data strategy. They will collect data to suit their needs rather than consider the implication of their choices on other departments that also benefits from that data.
The lack of ownership of the entire process makes it challenging to develop or maintain a data strategy.
5. Does not facilitate data-driven culture.
If every teammate has to rely on data analysts to procure insights, the data strategy is not facilitating a data-driven culture.
According to Simon Asplen-Taylor, CEO and founder at Datatick,
Organizations have the structure wrong and data scientists are being called in to solve problems they are not necessarily equipped to solve."
This process is complicated and the person doing the analysis work may not understand the full scope, or may not be invested in the outcome of the data as a whole but rather in completing the mission at hand- whether that be managing data or combing through data for analysis.
The issue of data literacy.
Data literacy includes an understanding of data sources, analytical methods and techniques, and the ability to describe the use case and the resulting value.
Data literacy is the ability to read, write and communicate data in context. A recent Gartner survey of chief data officers found that poor data literacy is one of the top three barriers in building strong data and analytics teams.
If a data stack is complicated, teammates may have context on data sources and resulting value but need support in the methods and techniques, which is where analysts come in. The more the team has access and ability to explore data, the more data-literate they become.
How Toric Can help
The main issues for creating data strategies boil down to the fact that the process to create a data pipeline has been too complicated for far too long. Instead, to create a data strategy there needs to be a tool that can transition an existing data stack into a single data pipeline. Toric can be your entire data pipeline or a process to get a birds-eye view of your data and data blending.
1. Toric can be your entire data pipeline.
Toric is an all-in-one, no-code data pipeline platform. You can pull from any data set, process the data, and more, all in a single cloud-based platform without any manual coding requirements. Learn more in our blog - ‘What Does a Single Data Pipeline Look Like?’
2. Easily manage and pivot with data.
With the ability to pull data from any source, blend it, and store it all in one platform, you can easily create an overarching data strategy with complete control.
3. Eliminate rework with repeatable and automated dataflows.
Part of your data strategy should minimize starting from scratch over and over again. Imagine if you could build a pipeline and then just replace the source data to get new results.
This function is particularly great for companies that have multiple projects. They can utilize a single data expert to create dataflows and allow their team to populate dataflows with their project’s data source and automatically churn out a report.
4. Leverage reusable Data Apps.
Use your team’s time wisely and create re-usable Data Apps. Teammates only need to swap out the data sources to create a new report. There is no need for a back and forth on the data pipeline through email, as you can annotate comments into a data app or within the data flow.
Creating a data strategy can be a massive undertaking, but with an increase of new tools that can replace or augment large, complicated data stacks it’s much easier to facilitate a cohesive data strategy. One that can pivot quickly to align to business goals and foster collaboration.