How to create effective data-driven strategies?

An effective strategy begins with understanding what stage of data development your organisation is at. Data maturity encompasses:

·      the quality of your systems and source integration;

·      a data-powered organisational culture;

·      the skills of your analytics teams;

·      the extent to which data are used in everyday management.

Models such as a big data maturity model enable you not only to identify the gap between the current status and what you are aiming for, but also to plan a realistic transformation road map, along with measurable KPIs.

Why does it work?

✔ Consistency, with the entire organisation operating on the basis of the same data.

✔ Measurability, with the effects of activities clearly defined.

✔ Flexibility, with a strategy that can be adapted to changing conditions.

For data to provide value, they have to be available, clear and useful not just to analysts, but to every department. The reporting environment should connect:

·      interactive dashboards (Power BI, Tableau, Looker) that facilitate independent KPI analysis and fast responses;

·      automated periodic reports, like automatically generated and distributed monthly sales reports, for example;  

·    data integration and quality, thanks to ETL/ELT tools like dbt and Apache Airflow. 

 The outcome: data-driven decisions are made at every level, from the board to operational teams.

Data silos restrict analytic potential. Consistent integration of various sources, like sales, marketing, CRM, e-commerce and social media, makes it possible to build a full picture of a client or customer and respond quickly.

Worth using:

·      data streaming, like Apache Kafka, for example, for sending data in real time;

·      cloud computing (Azure, Google Cloud), for scalability and centralising resources;

·      data flow automation, minimising manual interventions.

Benefits:

✔ dynamic marketing campaigns;

✔ real- time personalisation;

✔ operational automation of aspects like inventory management, for instance.

Predictive models and AI allow you to move beyond historical analysis to forecasting future scenarios. 

Examples of uses:

·      predicting demand, like seasonal sales, for example;

·      personalised marketing (recommendations, segmentation);

·      automating client/customer services (chatbots, lead scoring).

With behavioural and historical data analysis, solutions that genuinely support the growth of your business can be built.

Compliance with regulations such as the GDPR and responsible data management are the cornerstones of trust.

 Critical operations:

·      implementing data governance, in other words, the structures and policies of information management;

·      data anonymisation;

·      automating compliance processes and monitoring systems in real time.

This not only provides protection against legal risk, but also builds client/customer loyalty.

Agile data governance enables you to respond more rapidly to change.

 Crucial elements:

·      automated data quality control;

·      the ability to update policies quickly;

·      integration with CI/CD-type tools when working on data.

 An agile approach increases operational efficacy and reduces analytical implementation times.

Data are a shared resource. Leveraging their potential to the full means promoting collaboration by way of:

·      shared data platforms, such as cloud solutions for every department, for example;

·      workshops and hackathons for building awareness and exchanging knowledge;

·      shared KPIs, like measuring impact on the client/customer experience for marketing and sales, for instance.

Only close collaboration makes it possible to avoid discrepancies and inefficiencies.

A data-powered strategy should be iterative. It requires a reporting system that facilitates the ongoing monitoring and optimisation of KPIs.

 How is that done? By:

·      setting concrete KPIs, such as increasing conversions and client retention;

·      monitoring them using tools like Google Analytics and Power BI;

·      analysing the results regularly and implementing improvements.

This will guarantee that your data strategy grows along with your organisation.

A properly designed, data-driven strategy enables an organisation to:

✔ respond more rapidly to market changes;

✔ understand client/customer needs better;

✔ achieve greater operating effectiveness.

Data strategy is not an IT project. It is a long-term approach that combines people, processes and technology towards a single goal; growth grounded in facts. MakoLab’s offering encompasses the full data strategy workflow, from evaluation and organisation, via integration, to analytics and automation. We fuse our technological knowledge with our business acumen and experience to support our clients in building effective data-driven strategies that make a real contribution to their growth and competitive advantage. 

What is a data strategy?

A data strategy is a plan that sets out how a company leverages data to achieve its business goals. It includes a number of elements, including collection, analysis and implementing findings.

How do we evaluate our organisation’s approach to data?

By analysing the current capabilities, in other words, the systems, analytics and governance, and identifying the gaps. Audits or industry benchmarks can be helpful with this.

What is AI’s impact on marketing?

It makes it possible to personalise campaigns, predict trends and automate processes like real-time product recommendation, for example

Do you have any questions? Write to us!