Analytics is so much more than just a chart depicting the trend in some data.
Fundamentally it includes that simple chart generated in Excel and extends to the smarts behind Artificial Intelligence !
Rise of data analytics!
As you can see from the below chart the rise of “data analytics” has been quite steep and is gaining in popularity as has machine learning, predictive analytics and advanced analytics. All these are focused essentially on leveraging more value from the growing volume of data being generated in our lives.
The figures vary but regardless they are large. IDC, Forbes and others state the analytics industry is worth between US$48.6B to US$125B, again both indicating a CAGR (compound annual growth rate) of 20-30%. So there is a lot invested in this area!
More importantly are the financial returns available.
For example, technology research firm International Data Corporation (IDC) found in one study that analytical projects aimed at improving production had a median ROI of 277 percent (“Competing on Analytics”, Thomas H. Davenport)
Given this rise of importance in analytics and related fields, it is worth understanding it better and pursuing it.
So what is Analytics?
Well Wikipedia says that it is the “discovery and communication of meaningful patterns in data“. Modern data analytics is often thought to be found at the intersection of computer programming and statistics/maths clearly communicated through compelling data visualisation.
…and who wields this awesome power ….
The person that shapes the analytics can be either a data analyst or scientist. I subscribe to Sebastian Thrun’s (@ Udacity) suggestion that the data analyst applies statistical/mathematical models & functions to real world problems, where the Data Scientist title reserved for someone who not only does this but also develops new techniques / methodologies. A data analyst needs to have a combination of skills/knowledge in computer science (programming), mathematics (incl. statistics) and domain expertise (knowledge of business).
Although the key aspect here is the discovery and communication of meaningful patterns in data, it should never be forgotten that Analytics has no value if it is not directed at ACHIEVING SOME OBJECTIVE for real world outcomes.
Some of the objectives or applications of data analytics are as follows:
- Marketing optimisation
- Product & service recommendation – cross sell & up-sell
- Customer defection & acquisition
- Fraud detection
- Credit Risk management e.g. likelihood of loan default
- Predictive maintenance of machinery
- Energy usage in buildings
- Employee defection & acquisition
source: Microsoft Cortana Analytics
Why is it on the rise?
Business is surrounded by change and there are several specific factors that are having an immediate impact. These factors are:
- Cloud Services
- Digital Footprints
- Pervasive Intelligent Compute
- Competition & the Pace of Change
- Learning Organisation
Let’s go through them one by one.
- Growing digital lifestyles and expectations of personalised experiences and enhanced services / relationships. There is dual benefit in society here. Firstly for companies there is less money wasted on activities and services that are generalised. On the flip side for the consumer, the more personalised the service the more valued it will be. Take Google advertising as an example. If their advertising was highly generic, then you are likely to be annoyed by the constant barrage of advertising. However the smarter that advertising becomes the more likely you are to accept or event welcome the advertising… it becomes a blur between Advertising and Concierge. In this example by scanning your transactional activity (emails & search) Google can identify that you are probably going on an overseas holiday and as such advertising about upcoming events in that city/country as well as present travel insurance options that maybe useful information for you.
- The benefits of Cloud Services such as Microsoft Azure are scale, elasticity, flexibility and total cost of ownership
- Cloud Services make it easier to develop and build such personalisation services and to be able to scale these as required.
- You also have the flexibility of adopting footprints of varying levels of control from Infrastructure As A Service (IAAS) e.g Azure Virtual Machines, Platform As A Services (PAAS) such as Azure SQL Datawarehouse and Azure Machine Learning, and Software As A Service (SAAS) e.g. Power BI.
- From elasticity comes the support for agile and iterative learning business models. New virtual machines for technology implementations can be created and removed on demand, reducing cost of ownership whilst facilitating experimentation.
- Explosion of data. It is said 90% of the world’s data created in the last 2 years. This data comes from social networks, emails, websites, blogs, cloud services.
- In part this is driven by mobile adoption of cloud services, but increasingly data growth will come from new applications of technology including sensors.
- Society is moving from product consumption towards services consumption. More and more of these services are being digitised and because of this they are being delivered to global markets as a commodity.
- An increasing amount of data (aka Big Data) puts in front of us the challenge on how to processes and extract meaning… how to discover those patterns.
Pervasive Intelligent Compute
- Increasing compute capacity spread across mobile and cloud combined with increased data availability is leading to improved advanced algorithms and analytical capabilities.
- The Internet of Things, which is the incorporation of everyday physical objects onto the internet), growth such as sensors (in clothing, vehicles, appliances, etc) will provide a greater flood of data. This will be exacerbated by the pervasive growth in devices that incorporate micro and nano computing capabilities.
- It also means the development of products and services that we haven’t even thought of yet, as we seek to creatively combine current and future technologies, all of which will leave behind a data tsunami.
Competition & the Pace of Change
- The behavioural motivations of business, government and individuals drives the pursuit of more effective technological capability. For business this is the pursuit of competitive advantage and the profits that follow. However improvement in technological tools such as cloud services has brought greater agility in the delivery of creativity, differentiation & ultimately value to market… meaning shorter design and delivery times as well as faster iterative improvement in products and services being the delivered. All this amounts to a constant and increasing pace of change.
So we come back to data analytics. Where does it fit in?
Fundamentally the foundation of a digital environment is data. And even when that world is physical & real, we seek to record, measure and describe it using data. Organisations seek to improve performance and attain a competitive advantage and Analytics (data) provides that capability and enables them to influence and shape better real world outcomes whether that be an improvement in sales, better performing workforce, more effective advertising or fraud avoidance.
Organisations that have an analytics strategy and capability are highly correlated to have better performance. Indeed those that do not have this capability are at a significant risk of becoming out-competed, disrupted and destined for a corporate graveyard !