Stop wasting time on collecting and managing data that has no impact on your business!
Confirm what do you need data for, what specific data points are critical to support your business, assess how can you collect and process different type of data, plan how you can use it to generate impact on your business. Business architecture design for data management is critical for all organizations, also yours!
In rapidly evolving business landscape, data has emerged as one of the fundaments for the success, holding immense potential to drive growth, innovation, and efficiency. Navigating the new data-driven era is however not an easy task. Many organisations, even though they try to do it the right way, miss basic capabilities to work with data across different functions and business units. At this moment, companies leverage at bast approx. 20% of the data they collect. To really change improve their business performance they should double that. On top of that, there is an issue with understanding of what data is needed to be collected. Taking this into account, effective data management is a cornerstone for success. The first step on the way to become a true data driven company is crafting a well-defined Data Strategy. With that done, a purpose-built data technology ecosystem must be put in place and complemented by strong service model that enables the organisation to benefit from the new capabilities. Each organisation must also recognise that the new way of working is not only about technology and processes, but also about their adoption. This can unlock a powerful toolkit that not only fuels sales but also optimizes overall operations.
The Power of Data Strategy
A Data Strategy is a crucial roadmap for how an organisation will acquire, store, manage, analyse, and utilize data to achieve its objectives. It is the foundation upon which a data-driven organisation is built. Data Strategy allows organisation to decide what data is to be processed, avoid waste of funding in future Data Technology set-up, limit business risk – especially in case of handling personal identified information (PII data) – and establish basis for the adoption across the company. Data Strategy is key to establish optimised Conceptual Data Model which afterwards can be translated into Logical Data Model and finally a Physical Data Model.
Well-developed Data Strategy benefits companies in multiple ways:
- It allows Informed Decisions: Data Strategy makes sure that all the data is collected, processed, and transformed into actionable insights that can drive business actions. Data empower decision-makers make informed choices, reducing guesswork and driving better outcomes.
- It develops better Customer Understanding: By leveraging data and data analytics, companies can gain a deep understanding of their customers’ preferences, behaviours and needs. This knowledge enables tailoring of products and services, leading to improved customer experiences and ultimately increased
- It optimizes Operations: Data enables organisations to streamline their operations, identify issues, and optimize different This increases operational efficiency but also reduces costs, increasing profit.
- It enables Predictive Analytics: Through advanced data analysis, businesses can predict future trends and customer behaviours. This proactive approach allows for the anticipation of market shifts and changing demands, ensuring timely adjustments to strategies.
Building a Data Technology Ecosystem
Translating the Data Strategy into a well-designed data technology ecosystem is a crucial step towards maximizing its benefits. This ecosystem comprises the tools, platforms, and infrastructure needed to manage and analyse data effectively.
Data processes to be supported by technology:
- Data Governance and Security: First and foremost, ensure data quality, security, and compliance by putting in place data governance practices as of day 1. Data privacy regulations must be carefully followed, especially when you handle any consumer data.
- Data Collection: Data collection mechanisms are crucial to capture relevant information from various touchpoints. Every company needs to decide what data is to be collected and how in order to be used in the future. It is also important to establish clear structure of the data that can be accepted and further processed by the organisation (for example specific address format that can be accepted or not).
- Data standardisation and deduplication: Whenever data is collected, make sure to apply data quality process on it. Every time data is coming to the data base, it needs to be standardised (the format of the data needs to be checked and adjusted to the format used in the data base, for example the house number must be separated from the street name and placed in a different data field). Standardized data should be deduplicated vs existing data set, before it is loaded to the data base. This allows to avoid duplicates and helps keeping consistent view on the specific data object.
- Data Integration: Integrate data from different sources from outside and within the organisation. Break down data silos and try to create single view of specific data object – for example consumer (single consumer view) or product (single product view).
- Data Storage: Collected data must be stored securely in an easy to access data base, either on premise or in a cloud infrastructure. Cloud platforms are commonly used for scalable and flexible data storage and processing. Cloud services provide the agility needed to accommodate changing business needs without heavy upfront investments.
- Data Analysis and Visualisation: Advanced analytics tools help to extract insights from the data and make them available for the decision makers. Visualisation techniques, such as dash-boarding and reporting make these insights understandable for the decision makers.
- Machine Learning and AI: Machine learning and AI algorithms help uncover patterns in data and provide predictive analysis. These technologies enable personalised marketing campaigns, demand forecasting, and more.
Whenever data strategy is being translated into technology stack, cross-functional collaboration is key. Foster collaboration between IT, data science, marketing, sales, and operations teams.
Conclusion
Strategic approach to data management is pivotal in today’s business. Establishing a comprehensive and consistent Data Strategy as well as implementing a tailored data technology ecosystem empowers companies to harness the full potential of their data. By leveraging data to boost sales, enhance customer experiences, and optimize operations, organizations can position themselves as leaders in their industries while adapting swiftly to changing market dynamics. Embracing data-driven decision-making is not just a trend but a strategic imperative for future success.
