With a clear framework, best practices, and case studies.
With a clear framework, best practices, and case studies.
Modern enterprises are struggling with an overabundance of raw data and underutilization of data assets for achieving business objectives.
The right data strategy helps you unlock the hidden potential from the stored-but-seldom-used enterprise data.
In this article, you will learn:
A data strategy is a plan defining long-term data objectives, as well as the data architecture, tools, talent, and short-term initiatives needed to achieve them.
But don’t mistake a data strategy for your current data tasks. The most recent Jira tickets on a miscalculated metric or failing data integration job are not part of the data strategy.
Unlike traditional data management and project management tasks, a data strategy:
To better understand how a data strategy looks in practice, let’s look at its key features.
No two enterprises will have the same data strategy.
But every good data strategy will include the following key components:
A successful data strategy will benefit corporations in multiple ways.
The right data strategy helps your organization to:
Let’s dive deep into the framework of how to build and deploy a successful data strategy.
The following framework outlines the 7 crucial steps for building a good data strategy:
You should align the main purpose of your data and data strategy with your company’s long-term business goals and business strategy.
Having a clear prioritization framework will help you resolve conflicts about what should be implemented first and give you leverage to say no to data initiatives that do not contribute to your data strategy goals.
For example, imagine you are approached by both the VP of Sales and the VP of Logistics with their requests. The Sales leader wants the data team to automate data collection from CRMs and other sales data sources so they finally understand the prospects they’re working with, while the VP of Logistics asks you to clean the metadata in their data sets to remove confidential information when sharing data with supply chain partners.
Both are valid requests. Both can further business goals. But both require a lot of resources. Having a clear data purpose will help you navigate this prioritization dilemma.
If you’re uncertain about how to translate the business strategy into data purpose, tap into the two types of data strategies by Harvard Business Review: offensive vs defensive data strategies.
Defensive data strategies focus on minimizing downside risks - ensuring regulatory compliance, using analytics to detect and limit fraud.
Mall Group, the leading e-commerce group in Central Europe, used Keboola during a massive company expansion with the purpose to consolidate and streamline their operations. They purchased and integrated over 30 different companies servicing over 5M customers within 24 months. With Keboola, the team managed to not only integrate the unimaginable complexity of 30 different companies during the mergers and acquisitions. But also set up impeccable data security, regulatory compliance, and data governance throughout their data operations. Check the interview with Mall Group’s Head of Business Intelligence for details.
On the other hand, offensive data strategies focus on achieving business objectives such as increasing revenue, profitability, and improving customer experience.
Česká spořitelna is the biggest Czech retail bank. Traditionally financial institutions focus on defensive strategies. But Česká spořitelna wanted to gain a competitive edge. So they used Keboola to build offensive growth engines such as optimized lead generation across the omnichannel, hyper-personalized marketing at scale, and automated credit risk scoring. Check the full story here.
All companies will have to strike a balance between defensive and offensive approaches. But understanding which one your data strategy prioritizes helps you build a prioritization foundation.
A gap analysis helps you identify what technologies, talent, or processes you’re missing to elevate the current state into the desired state.
To better understand your goal state, rely on Gartner’s data maturity model which specifies how organizations progress through different stages of data growth: descriptive, diagnostic, predictive, and prescriptive.
Not all company units will be at the same level of data maturity. Some departments will use big data predictive algorithms in their work, while others will struggle to track basic performance metrics.
The general philosophy is to identify the lowest level of the data maturity model throughout the company, fortify it, and then move to the next stage.
Once you’ve established your main purpose (step 1) and identified what needs to be changed (step 2), you can prepare a proposal for implementing your data strategy.
Your proposal will outline all the key components:
Executive sponsorship will be crucial for the success of your data strategy.
To make your case for change more appealing, focus on the return on investment gained by implementing your data strategy. For defensive data strategies, the value of data lies in costs saved and avoided. For the offensive data strategies, the value of data is derived from the additional revenue and profits.
To successfully implement your data strategy, you will have to build a team around your initiatives. But not every team member will be involved in the day-to-day operations. The right team will include:
Understanding what headcount is needed helps you plan the hiring and training initiatives to get the right team. For example, maybe your in-house data analysts need more training in data analytics to provide the visualizations for descriptive analytics. Or you might need to hire more data engineers to optimize the data pipelines used by data scientists to build a predictive model.
Best practice: A lot of the issues with data literacy can be solved with a data catalog. A data catalog documents every field in your data set, allowing people to more easily self-serve their data needs and understand the data assets they’re working with.
Keboola offers a Data Catalog that helps you go beyond documenting data. Keboola’s Data Catalog also binds itself to data, to be always aligned with data changes, and can easily be shared whenever you share data.
Schedule a call to learn more about it.
The data architecture will reflect the goals you’re trying to achieve.
Data architecture decisions cover anything from:
For example, let’s say you’re looking for a data storage solution:
Best practice: Picking the right tool for the job can save you time and money. For example, Hari built an artificial intelligence product with a 3x smaller team by having Keboola take care of every data process: from collecting data from various data sources, validating it, building predictive models, and finally embedding the machine learning algorithms into the product.
Step #6: Set up data governance
Data governance will sit at the intersection of your processes and data architecture. The data governance framework defines:
Data governance will sometimes conflict with data architecture. Within data architecture, there is a trend to choose the best tool for the job, thus increasing the number of specialized solutions for their use cases. More tools increase the difficulty of applying data governance across all those tools. So you need to make a tradeoff between increasing your tooling surface and keeping data managed.
Best practice: Governance and data architecture can both scale together with a common underlying infrastructure. For example, Keboola allows you to integrate multiple tools without increasing the burden on data governance. Keboola acts as a centralized command center through which you can set up governance rules for every process in the platform. All tooling can be tracked by following their lineage in Keboola. And security is guaranteed for all data.
Step #7: Implement the data strategy roadmap
Finally, you start implementing the roadmap for your successful data strategy. The roadmap will specify the milestones and timeline to achieve them.
Make sure to include some quick wins in your milestones. Those will act as motivators for your team and signal success to the business leaders supporting your initiatives.
Best practice: Don’t be afraid to change your data strategy when needed. Re-evaluate your progress regularly, and if the data strategy becomes unaligned with the business strategy, make sure to course-correct.
Keboola is the data platform as a service that automates the data processes supporting your data strategy:
Keboola helped many companies establish or revamp their data strategies at a fraction of the costs.
Wondering how Keboola can help you scale your operations into a better data future?
Get in touch and let’s start planning your data transformation together.