Data is the epicenter of every AI Strategy. It is the beginning and the end of every AI solution designed to tackle challenges.
How the sector creates, analyzes and uses this data is an integral aspect of the success of its objectives. Here, we present the essential aspects of data management and the key considerations required to use data effectively.
1. Data Inventory:
A data inventory is a detailed database of a firm’s data assets. This database is also called a data map because it houses information about the type of data available, its accessibility, use, and its role within the framework of the organization’s projects. It is vital that the data inventory is up-to-date and includes information about the data source.
A well-managed data inventory increases efficiency and ensures that data privacy laws are complied with.
Data mapping is complex and difficult due to the thoroughness required. To ensure a comprehensive data inventory, an assessment should:
i. Review existing and available data
ii. Determine its source and where the data resides
iii. Ascertain its accessibility and ease of this accessibility
iv. Verify its quality
v. Ensure that no existing data is being under-utilized
Once the above are ensured, a framework for data development should be developed to align with the organization’s KPIs.
2. Data Collection:
Data collection is the systematic gathering of variables of interest to answer specific business or research questions. Data can be gathered primarily or secondarily. In the first instance, the firm directly collects information from its users while in the latter, the data source is an external party e.g. an open source website. Both methods have their pros and cons. A variety of factors, including time and economic resources, will play a determining role in choosing which data collection form to adopt.
In creating a data collection strategy, the team’s work should focus on:
i. Defining the objectives and the type of data required to meet those objectives
ii. Assessing all necessary factors needed to choose whether to gather the data primarily or secondarily
iii. Planning data collection procedures
iv. Operationalization i.e. converting abstract ideas into measurable entities
v. Ensuring ethical and fair data gathering
vi. Automating data collection procedures
vii. Harmonizing this data with other sectors for easy collaboration
3. Data Quality
Data quality is the fitness of data to serve a particular function. It is defined in terms of completeness, accuracy, uniqueness, consistency, validity, and timeliness.
Guaranteeing data quality is an important part of ensuring overall data integrity.
Across all projects in the sector, the leading team should:
i. Carry out data profiling to initially assess and understand the data
ii. Standardize heterogenous data formats
iii Scrub data to ensure its quality across all six factors earlier mentioned
iv. Create systems to ensure timely data auditing
4. Data Pipelines
Data is constantly created in large quantities, speed, and diversity. A data pipeline is a network system that facilitates the automated transfer of data from a source location to a target destination. Data pipelines help to automate and scale the process of data acquisition, transformation, flow, and integration. In AI applications, data pipelines may also be used to train datasets for machine learning.
Here, the team can ensure that:
i. The sector’s data pipelines are designed to allow the smooth flow of data to meet its objectives.
ii. Data is migrated from siloed sources to a centralized location to create clean, secure, and compliant data for use.
5. Data Science Teams
Artificial Intelligence is brought to life by human creators. The sector will need to lean into harnessing human resources to create a Data Science team to actualize its aims.
An ideal Data Science team comprises the following:
i. Machine Learning Product Manager works with the ML team, users, and data owners to prioritize and execute projects.
ii. DevOps – to deploy and monitor production systems.
iii. Data Engineer – builds data pipelines, aggregation, storage and monitoring systems.
iv. ML Engineer: They train and deploy prediction models.
v. Software Engineers: Frontend/backend development, integrating new AI features to existing products
vi. Data Analyst: They answer business questions using analytics.
vii. Data Visualization Specialists: They have a strong design aesthetic and are responsible for creating infographics, dashboards, and other design assets.
viii. Domain Expert: Designing success metrics, feature engineering, and validating the ML system results.
Best practices involve:
i. Establishing guidelines for a successful hiring process
ii. Training data science talent on the non-technical skills required to build working AI models
iii. Teaching AI Citizenship to team members.