Let’s accept it. Data is jostling for as much attention in budget planning as any other important aspect, say marketing or resources. While it is true that a spotless data reservoir would be the dream of the Chief Data Officer (CDO) of any successful venture, the expenditure required for flawless input, processing and management of data often exceeds the allocated budget by a long jump.
So, is there a solution?
1. Acknowledging the value of data: The foremost step is to understand how important data is for an organisation and to share that knowledge with all participants – the stakeholders, employees and everyone else. In today’s world of big data and analytics, there is almost no industry where data is not the key. Data holds the insight to propel a business forward.
Automated processes and integrated systems are today being used to enhance efficiency and support employees in a better manner. The most obvious example is marketing, where marketing campaigns cannot be made without depending on information about the demographic, product value and so on – all of which is data acquired through various means. If this data is inaccurate and messy, imagine the loss that will be incurred by an ineffective marketing strategy. That is why the management of data is so important.
2.Finding a balance: While allocating a budget for data maintenance, it must be kept in mind that data naturally decays at the rate of 2% per month. Under investment in data quality can lead to poor insights, which in turn can cause flawed decision making, poor ROI in marketing and sales, and not to forget – excessive wastage across departments.
There are different costs involved in maintaining data quality, starting from data quality software to resources that can integrate systems. All this has to be kept in mind while finding a balance between the negative impact of inaction and shifting all the budget towards prevention of bad data.
3. Spending and saving: By spending on automation, training and hiring, one can save on data maintenance in the long run. However, one must never aim for zero errors in data as it would be too expensive. A more feasible option is to aim for data that is fit for purpose.
Automate the process of paperwork and combing out errors by purchasing data quality software. Training employees about big data can bring a change in the culture of how data is treated. Hiring a CDO can elevate that change up to the boardroom level and steer a new course of how data is dealt with and how insights are churned from it.
Data, and the insights provided by it, hold the key to the success of several departments under an organisation. And as the article explains, improvement of data is certainly a worthwhile arena of investment, when perfection is out of budget.
What do you think?