A Chocolate Lava Cake is very rich, very transformative, and pretty hard to make. If you have attempted to make a lava cake, you know, that for it to come out right, the recipe needs to be followed to the dot, using the right ingredients properly, and being methodical every step of the way. And when done right, the results are magical. However, one tiny error can make it fall apart.
Data Monetisation works the same way. You need to have a set procedure in order for your data to be accurate, usable and secure. Otherwise your insights will be worthless and even misleading. However, unlike baking that cake, managing your data can be extremely complex and the likelihood of making an error is high.
Data management and integration is a high priority investment
In order to leverage the maximum benefit of your data, and prevent costly mistakes, it would be good to start with a solid data management and integration strategy — a well defined set of policies and procedures meant to provide the right individuals with timely access to accurate data. Data arrives from hundreds of internal and external sources, providing the potential for game-changing insights within your organisation, if its managed efficiently.
Data Governance (the recipe)
Data governance establishes the procedures, standards, and policies, which set the parameters to successfully manage your organisation’s data. An extensive data governance plan defines user rights, security policies, and monitors the technologies used to implement the various data procedures. It’s the blueprint on how your organisation manages its data (with a focus on people, policy, and technology).
According to Forbes, data scientists spend 80% of their time identifying and processing data to use before doing any analysis. Good data governance reduces this by providing a plan and structure for data to be secure, easily found, and shared among individuals with the appropriate permissions.
Quality Ingredients (it always makes the results better)
A Lava cake is actually not that difficult to make, IF you know how to prepare the right ingredients. For instance, the most important ingredient is egg whites, but if the egg whites have any traces of egg yolk, your lava cake is doomed.
If your data is tainted, your insights are going to be far from reliable. Data placed in the wrong field, or data with spelling errors is a problem that can often go unnoticed. The power of your analysis is only as good as the quality of your data. Without proper policies in place to ensure data quality, you will most certainly have costly data inconsistencies.
A DMI program should include a data cleansing procedure to resolve inconsistencies. Data cleansing tools can eradicate duplicate (or incomplete) data points. Your data is generated across a lot of touchpoints, and therefore increases the probability to risk of duplication and redundancy. But a proper DMI approach assures that your data is clean and ready for use—enabling you to have the confidence to make decisions based on your analysis.
Data integration (the preparation)
When you are following a recipe, it’s sometimes tempting to throw away the instructions, and mix in all the ingredients together at the same time. If you have done this before, then you know that it never ends well.
Integrating your data is no different. Data that comes from all your applications and legacy systems are not all compatible. You would of course need to combine some of this data to run your analysis, but you can’t just throw it in all together willy-nilly and expect accurate results. Data integration is a sophisticated venture when you think about all the structured and unstructured data that’s available to analyse. There is no cookie cutter approach. However, there are several tools to help automate the integrating, cleaning, matching, and preparing data for analysis.
However complex and technical data integrations might be, at the core of this are three steps called ETL (extraction, transformation, and loading).
- Data extraction is when data is collected from numerous data sources.
- Data transformation is the process of data cleansing, and transforming them into a proper storage format or structure for the purposes of querying and analysis, usually utilising a universal data model.
- Data loading is the insertion of data into the final target database such as an operational data store, a data mart, or a data warehouse.
There’s a lot of valuable data available to you, however, you can’t just throw them into an analytics tool and hope to make sense of it all. Your DMI strategy needs to determine the approach and tools necessary to implement a successful integration. When the ingredients are prepared and combined properly, your chances of accurate insights and a successful lava cake go from good to great.
Security (Protect your cake at all costs)
After painstakingly preparing your cake, you don’t want any unauthorised people in the kitchen to mess with it. If you turn your back for one second and someone opens the oven door, both your lava cake and your mood will be deflated.
In the same way, massive data breaches that compromise the personal information of millions of people are quite common. One of the biggest data risks within your organisation has to do with access management. A recent data risk report found that 47% of companies had at least a thousand sensitive files open to every employee. Unknowingly giving too much access to employees is a very common problem, with severe reverberations. For example, American retailer Target had a data breach where more than 40 million credit cards (including pin numbers) had been stolen. Amongst the many reasons for this, a report identified that too many people had access to sensitive data, including outside contractors. Source
Identity and Access Management (IAM) tools are designed to help you manage access to your data.
It’s used to authenticate, authorise, and track users that interact with your data at all times, and setup rules to comply with your data management policies. For instance, your IAM tool ensures that your staff and contractors can access data remotely and on any device securely, by adding extra security measures, such as automated logouts and multifactor authentification. And if a device gets lost or stolen, the data remains secure.
Conclusion (Just follow the recipe)
Just like how a perfectly prepared lava cake can transform a dessert, data-driven insights can provide your organisation a competitive advantage that elevates it to its next levels of productivity, efficiency, and success. Having solid data monatisation policies and procedures in place ensures your data is accurate, accessible, and secure — realising the power of your data to its fullest, and enabling you to make decisions with confidence.