The most efficient thing you can do if you are a doctor and want to prescribe effective treatment is to make an accurate diagnosis. Although it may eat up to 1 month of your time, seeking first to understand and diagnose before giving a prescription is a correct principle that saves a business a lot of money in the long run. If you don’t have confidence in the diagnosis, you won’t have confidence in the medication.

A data audit for data innovation is a process that seeks to diagnose and later provide recommendations that facilitate the realistic collection, processing, usage, securing transmission, and storage of data in a manner that supports affluent innovation and attainment of expected ROI.

 90% of all data analytics projects fail because businesses have not put in the time to understand whether the existing data assets can help the business achieve its strategic milestones. Data assets simply refer to all components that are involved in the capture, storage, and consumption of outputs of a business process which could be an application output file, document, database, or teams that companies use to generate revenues. Data assets are some of the most valuable assets in the technology era, and organizations spend billions of dollars to manage the assets.

A data audit takes an inventory of your data assets and provides guidelines on its usability. Critical to performing a data audit Do you know what data you have available? Are you making the most of it or Do you know how to make use of it?

A data audit seeks to answer three key questions.

1. From your existing data (primary & secondary), are you able to address all your strategic objectives?
2. Are there any anomalies in your data that could prevent you from achieving your business goals?
3. What is the ‘Rich & Reachness’ in your data?

This is so far the most important stage before a business invests in a data project. A data audit for data innovation is a process that seeks to provide recommendations that facilitate the realistic collection, processing, usage, securing, transmission, and storage of data in a manner that supports affluent innovation and attainment of expected ROI. To perform a data audit for data science means being able to:

1. Review all existing data and data sources (both primary and secondary).
2. Match strategic objectives with data assets available.
3. Identify gaps in the data ecosystem that may prevent the organization from achieving its strategic objectives and recommend the way forward.
4. Identify additional innovation avenues that the organization has not thought about also called the “Data-Rich & Reach”.
5. Assess data collection tools and data pipelines.
6. Assess organization-wide data storage and protection needs.
7. Assess data collection points, identify gaps and recommend corrective measures hitherto.

To jumpstart your data innovation journey and realize a profitable venture, seek to create a data strategy that works for hand in hand with a data audit for data innovation. The data innovation strategy will create an ideal roadmap bespoke to your organization's current and future market positioning. The deliverable will present a report which maps strategic pillars to profitable initiatives, creating a clear roadmap on products, services, and data innovation for future growth and profitability.

To be sure of your data investment, the data audit will identify content and structural gaps in the data ecosystem that may prevent the organization from achieving its strategic objectives. The deliverables will consist of a detailed report with recommendations and adjustments in readiness for data innovation.