When we talk about Data and Analytics, conversation often turns to topics such as Machine Learning (ML), Artificial Intelligence (AI) and Internet of Things (IoT).
Whilst these are subjects that excite quite a few of us, it is important to know that there are a number of things that organisations have to get right before they can truly get the most out of Analytics.
There are 12 key components in building successful Data and Analytics Capability.
1. Roadmap and Operating model
An Operating model turns a Vision and Strategy into tangible organisational outcomes and changes. It is a single view of the capabilities within an organisation and the way in which they deliver services internally, and to their customers.
Without a robust operating model, organisations will not have a sustainable design for the structure, processes and capabilities needed to manage data effectively and benefit from the insight generated through the application of analytics.
2. Platform and Data Architecture
The right platform gives organisations the ability to store, process and analyse their data at scale. Modern, open-source data platforms developed by the likes of Facebook, Yahoo and Google have made data storage cheaper, whilst making data processing far more powerful.
If Data is the Fuel, Analytics the Engine, then the Platform is the Chassis.
3. Data Security
Organisations need to ensure their data is stored, transformed & exploited in a way that doesn’t compromise security.
Data security, and the consequences of getting it wrong, is a hugely important part of a data and analytics journey. Insight and analysis should not come at the expense of data security.
4. Data Governance and Standards
Data Governance is one of the least visible aspects of a data and analytics solution, but very critical. It includes the management and policing of how data is collected, stored, processed and used within an organisation.
Effective governance is not a one-time exercise, but a fully developed and continuous process.
5. Software and Tooling
Whether it is a simple report or performing advanced machine learning algorithms, an analyst is nothing without their tool.
Finding the right combination of tools is a challenge – there are a lot of them! That means considering everything from the Techniques analysts want to apply, to how they fit in with your Data Security and Data Architecture etc.
6. Legacy Migration
Organisations may need to migrate and transform legacy business services onto a new platform to deliver new insight at a lower cost.
When an organisation takes the bold step to upgrade their data or analytics capability they might think the job is done upon completion of the implementation phase. However, to drive the value from their investment, they also need to migrate existing analytical capabilities and services to their new technology.
7. Data Acquisition
Data volumes are exploding; more data has been produced in the last two years than in the entire history of the human race.
Traditional business data sources, such as data from CRM, ERP systems etc. are being enriched with a wider range of external data, such as social media, mobile and devices connected to the Internet of Things.
Organisations need to identify which data sources will add the most value to them, and develop ingestion patterns that make them easy to access and safe to store.
8. Skills and Roles
It is becoming increasingly difficult to find the right skills in order to put data and analytics at the heart of organisations. “What does a data scientist do?” “Where can we find a data scientist?” “What skills do our people need?” These are the questions that need to be answered.
Since people are the most important part of any business, so hiring the right people with the right capabilities, giving them a platform to improve & develop and keeping pace with industry best practice / new technology is critical.
9. Business Intelligence and Reporting
It is vital for organisations to understand their performance, identify trends and make informed decisions at all levels of management.
Without a strong BI capability, we will not be able to detect significant events or monitor changes, and therefore adapt quickly.
10. Insights and Analysis
Many organisations are acquiring more and more data from various sources. However, data is only valuable if we can extract value from it.
Insights and analysis allows us to rapidly get valuable insight from data using visualisations to spot trends allowing us to make critical business decisions based on facts which can give us a competitive advantage.
11. Real-Time Analytics
Industry leaders are moving towards Real-Time, Probability based and Predictive Analytical approaches. Organisations can now deliver ‘Real-Time’ analytical capability to have the best of both worlds; digital customer experiences that are analytically assessed and secure.
This is a change from reactive organisations to one that actively drives proactive interaction with customer through real time, in the moment, analytics.
12. Advanced Analytics
The pinnacle of a data and analytics capability is the application of advanced analytics to discover deep insights, make predictions and generate recommendations.
Predictive Analytics, Text Mining, Machine Learning and AI are all making great strides across all industries. With the right people, data and technology, organisations will be able to take advantage of these capabilities.
The important thing about all of these components is that they can be improved individually. Building up your data and analytics capability is not about huge transformational programmes, but about incremental step changes in each of these components.