What’s your company’s biggest asset?
Is it the people on your team?
Your market share?
It could be argued that none of those things would be possible without your most valuable asset, data.
It’s estimated that there are about 2.5 quintillion bytes of data created each day. That breaks down to about 1.7 MB of information per person every second.
The majority of that data is collected by government agencies and businesses, which gather information for activities like demographic research, marketing, and long-term planning.
Every time you engage on social media, mine information through business intelligence or obtain customer feedback, you’re dealing with data. This information can be used to tailor the customer experience, plan and manage business growth, and increase profitability. Armed with the right information, you’re better able to identify and understand trends, improve business processes, and eliminate waste.
However, most of what you get is raw data, whether in the form of customer activity tracking, identifying information, or statistics.
But what do you do with all of that information, and how do you protect it once it’s in your possession? That’s where your data strategy comes into play.
Data management relates to how information is collected, stored, and used. Due to privacy and security concerns, there are laws and guidelines in place that govern what data you’re able to collect and how it’s managed.
Your strategy is a broader, more comprehensive set of guidelines that encompasses your data management system but also includes its architecture and the technologies used to implement data governance. It helps to align your data management activities with key processes and long-range business planning strategies.
In short, your strategy contributes to operational efficiency and profitability while improving the overall customer experience.
Acquiring the tools to mine the data and space to store it is not enough. In order to devise such a strategy, your core principles should be formed around these questions:
1. What are your core data assets?
2. How does the data add value?
3. What processes and technologies are used to extract value from data?
4. How effective is your data management system?
Crafted with these questions in mind, your strategy will be actionable, measurable, and refined.
The goal of your strategy should be to prioritize innovation, address the needs of users, reduce risk, and adhere to compliance requirements.
No plan is viable unless it furthers your business goals and objectives. Are you looking to eliminate waste? Improve operational efficiency? Manage risk?
What are the unique goals of your business, and how can your strategy help you to reach those goals? Which challenges do you need to overcome, and how will your strategy address them?
Break the strategy down into smaller, measurable objectives that build toward reaching larger, long-term goals.
Change is hard, and your data transformation affects many people inside and outside of your company. An effective strategy involves getting input and buy-in from key stakeholders, including staff, customers, and vendors.
- Company leaders want to know how the strategy will advance company goals. You need to justify the expense by demonstrating added value.
- Staff needs to understand how the strategy will impact daily operations. Demonstrate how it will improve customer service and streamline processes.
- Customers need to be informed about what information is collected, why it is needed, and how it will be secured or disposed of. Draft an effective data governance policy and make it publicly available.
Assemble a team to help draft and implement your strategy based on input from stakeholders. It should be diverse and include key players from every level of your organization.
Your data sources should align with company objectives and help you reach important benchmarks and goals. Where will you obtain information, and what technologies will be used to gather, parse, and refine that information?
For example, a goal of improving the customer experience should include strategies for implementing customer-facing policies and processes as well as acquiring the appropriate technologies.
Using the information obtained through evaluating company goals, devise a series of use cases for Big Data analysis. Examples include Customer Behavior and Sentiment Analysis, fraud detection, and predictive support technologies. Prioritize them in order of importance, with critical use cases being implemented first.
A roadmap is an outline of your strategy that considers your objectives and includes tools, technologies, and a timeline for data transformation. This may be the biggest and most time-consuming step, but it will help your team remain focused and ease implementation.
Focus on identifying gaps and crafting solutions to close them. For example, a skills gap could be addressed through staff training.
Data is only as good as your ability to apply it in a meaningful fashion. Without an effective plan for gathering, managing, and leveraging business intelligence, you’re left with a chaotic jumble of words and numbers dumped into a silo.
Our goal is to provide you with steps and best practices that will allow you to make sense of the information you collect and use it to formulate data-driven business decisions.
Author: Alice Wills