https://www.gartner.com/en/topics/data-and-analytics

Data and analytics involves the collection, storage, protection, management and use of data to support better decision making, robotic process automation and the management of opportunities/risks. Data and analytics is key to improve decision making, issue diagnosis at all organizational levels. 

What is “big data?”

Big data is data coming in high volume, high velocity and high variety. If analysed, it can generate  insights to improve decision making and overcome organisational challenges. Big data requires innovative forms of information processing and skillsets making data scientists and analysts highly sought after.

Examples of predictive analytics

The following are examples of combining the predictive capabilities of forecasting and simulation with prescriptive capabilities:

• Forecasting the risk of infection during a surgical procedure combined with defined rules to drive actions that mitigate the risk
• Forecasting incoming orders for products combined with optimization to proactively respond to changing demand across the supply chain, but not relying on historical data that might be incomplete or “dirty”

Data and analytics strategic planning

• Set the the mission and goals of the organization as these will inform the data strategy later.
• Determine how data analytics can contribute to the organization to achieve its goals.
• Identify the key steps to achieve business goals using data and analytics objectives.
• Build a roadmap for data and analytics strategic building and implementation.
• Implement that roadmap with relevant projects to build the data infrastructure and governance, provide training, hire skills externally and be aware of change/culture management needs.
• Communicate data and analytics strategy and its impact and results to win support for execution.
  
  

What is data literacy?

Before you can leverage big data and analytics, organisations need to reach a baseline skillset among staff. According to Gartner "data literacy as the ability to read, write and communicate data in context. It requires an understanding of data sources and constructs, analytical methods and techniques applied and the ability to describe the use-case application and resulting value".

Key point: Its not about training every employee to be a data scientist, it is about brinigng all staff to a baseline and supporting other cohorts. For example, junior staff may generate pre-defined reports, managers may create customer reports to solve tactical needs while directors/leaders strategically use data to explore opportunities for organisational growth.

What is data and analytics governance?

Data and analytics governance allocates decision rights and accountability to members of the organization. This ensures that organizations can value, create, store, access, analyze, consume, retain and dispose of their information assets appropriately. Data and analytics governance is beyond compliance, it is linked to the  overall business strategy. Data management systems are a key element to maintaining your organizations data assets, the following are key components.

Master data management (MDM): Business functions work together with the IT function to ensure the uniformity, accuracy, stewardship, governance, semantic consistency and accountability of the enterprise’s official shared master data assets.

Data hubs: These enable data sharing and governance. Producers and consumers of data connect with one another through the data hub, with governance controls and common models applied to enable effective data sharing. There are different levels in data management, for example a data hub focused only on master data but data catalogs are increasingly moving into the governance.

Data centers: These are large facilities that physically store servers.

Data warehouses: These collect transactional, detailed data and support predictable analyses

Data lakes: These collect unrefined data so it can be later cleansed and analysed.

Core analytics techniques

So, what can data analytics do for your organization and how can it do it, lets explore some of the key ways you an analyze your organizations data.

Descriptive analytics
This uses business intelligence (BI) tools such as data visualization and dashboards to explore past and current events. For example, the procurement manager can answer questions like, what did we spend on commodity X in the last quarter? and who are our biggest suppliers for commodity Y?

Diagnostic analytics
This uses data mining abilities to explore why past or current events occurred. For example, the sales manager can identify why certain staff exceed their targets while others do not.

Predictive analytics
Predictive analytics explores the future probabilistic of certain outcomes. It can help managers in forward planning by asking what is likely to happen and how likely is it to happen. Predictive analytics uses predictive modeling, regression analysis, forecasting, multivariate statistics, pattern matching and machine learning (ML).

Prescriptive analytics
Prescriptive analytics explores the best ways to reach a certain outcome or result. Think of it as asking 'how can we get there'. This allows managers to allocate appropriate resources to better outcomes.

If you enjoyed this blog, check out our blog on How to Monetise Organisational Digital Data.