The adoption of cloud, mobile technologies and social media is generating immense volumes of data. This presents marketers with an unprecedented opportunity to use data to gauge customers’ reactions to existing products and to anticipate requirements and preferences using predictive analytics.
Gartner reports that big data is now moving beyond the hype cycle and becoming ‘operational’. This view is borne out by many large organisation, who are already using big data and associated tools to collect, manage and analyse data being generated by machines and meters, unstructured text, social media, wearable and location-tracking devices and website transactions and logs.
So, how can marketers turn all this raw data into revenue?
1. Make data trustworthy
Before making operational use of all your raw data you need to be certain that it is, in fact, correct. This is why master data management (MDM) is the first stage of the data monetisation process.
In the words of business intelligence analyst and founder of the Boulder BI Brain Trust, Claudia Imhoff: “MDM is the fundamental underpinning for a trustworthy and reliable analytics environment. MDM not only ensures consistent information about customers, products and other major data domains across all systems, but also ensures business users obtain results they can unequivocally believe in.”
MDM tools that add a metadata layer make it easier to validate and audit outcomes. We refer to this first step as ‘harmonising’ data.
2. Make data usable
In order to extend and operationalise your organisation’s data, you must make sure non-technical users can access the information they need, so it can be used to inform better decision-making.
A point to bear in mind is when non-technical, operational employees want to generate their own reports to gain insight from organisational data, apps tend to offer a more effective delivery mechanism than enterprise software tools.
By providing your colleagues and customers with simplified access to trusted data, you can start to gain a wealth of insights that you may not have even anticipated. This extends the value of your data beyond the domain of data scientists and puts it into the hands of people who are dealing with your customers and listening to their requirements on a daily basis.
3. Make data visual
Data usability is the key reason behind our focus on enabling self-service analytics with visual data discovery. Providing employees with clear visualisations of data empowers them to use their sector expertise to identify trends, risks and opportunities even if they have limited experience in using data analytics technology. This is particularly valuable within smaller organisations that may not have the budget to employ an in-house data scientist.
By combining interactive, visual data discovery with rapid dashboard creation and robust reporting, employees can start to measure and even predict customer trends and demands, combatting waste and inefficiencies and facilitating new services. As new data sources, such as wearables, in-car analytics and healthcare apps become mainstream, the value of putting in-document analytics in the hands of your employees will become even clearer.
4. Turn data into revenue
Monetising data is the most important part of the data management and data analytics journey. This is where your employees and partner organisations can focus on the needs of customers and generate the greatest ROI.
To provide two examples: Ford uses data analytics to anticipate when customers’ car components will require replacement, enabling the warranty departments to eliminate waste and reduce costs. Paints and coatings manufacturer AkzoNobel uses data management and analytics across 18 global business units to gain business intelligence (BI) on supply chain transactions.
It uses this BI to report on any supply chain impacts and make predictions, so that partners and customers can be kept informed of any potential issues in the delivery of raw materials. AkzoNobel reports that its use of BI and data analytics enables it to make a simple database entry to record any changes.
This has saved on time and costs that were previously accrued through hand coding supply chain data. The manufacturer can now focus on managing supplier relationships rather than integrating vendor-managed inventory systems and various enterprise resource planning systems.
Conclusion
Embedded business intelligence fuels many of the most popular websites used in banking, online trading, logistics, education, healthcare, government services and web marketing today.
The golden rules for success are to harmonise your data, then make it easily accessible and understandable to non-technical users and data scientists alike: using apps, graphics, familiar report formats, dashboards and in-document analytics.
Putting interactive data visualisation into the hands of all of your users really gets your organisation’s data working for you. Some of the most successful data analytics deployments result from data analytics being delivered to the least likely users.
As IoT innovations come online and start contributing to the data deluge that was started by cloud, mobile and social media, these four key steps will guide your organisation in identifying the diamonds in your organisation’s raw data.
