Data Strategy to Leverage Artificial Intelligence in Corporations

  1. Data Strategy. Understand which data assets are most relevant in your company, identify which already exist and which have to be created in the future. Link the data assets to your key business metrics, such as revenue, costs, customer satisfaction, market share, etc.
  2. Data Engineering. Clean your data and preprocess it in order to ensure that a meaningful link can be made during the next step: Data analytics. Moreover, investigate the potential of combining different data assets
  3. Data Analytics. Analyse the relationships between your data sources and your key business metrics. Only meaningful relationships should be explored further.
  4. Machine Learning. A) Create in-depth, non-linear and multivariate insights through machine learning and B) build powerful machine learning applications to improve business cases.
  5. Ensembles. Combine different (machine learning) models for maximum performance.
  6. Learn & Repeat. Draw a resume, adjust your data strategy and restart the process.
THE NEWNOW STRATEGIC AI PROCESS

1 Data Strategy

2 Data Engineering

  1. Is the data quality sufficient for exploiting the data opportunity?
  2. Is the data quantity sufficient for exploiting the data opportunity?
  3. Can data assets be linked through unique identifiers? If not, can additional code be generated to make data assets linkable?

3 Data Analytics

4 Machine Learning

5 Ensembles | 6 Learn & Repeat

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