More and more businesses are waking up to the potential value of the data they hold, but the next stage in the journey is realising what it means to be truly ‘data-driven’, fuelling growth and investment through their data insights. Interestingly, a recent report from Accenture found that data-driven organisations are actually growing at an average of more than thirty percent. Such results are desired by many, some of whom are still struggling to deliver high-quality data and insights across their entire business.
The extent to which a company can become ‘data-driven’ is often tied to its ability to access vast amounts of data and use expensive analytics platforms to yield insights for strategic business decisions. However, it is not just the organisations with the most data and latest technology that win - it’s also the organisations that take the time to be strategic, cleaning and transform raw data prior to its analysis. In fact, these organisations find the greatest value in their data because it is clean, complete, and free of errors.
The pressure to become data-driven
With greater expectations for businesses to stay relevant in their market and provide their customers with new and unique experiences, data-driven decision making has emerged as a differentiator. Leaders will look to the meteoric rise of tech giants such as Facebook and Google and their mastery of vast quantities of data and become accustomed to thinking that ‘the more data, the better.’
Although data quantity and speed of analysis is important – quality should also be prioritised. It is easy to fall short when it comes to presenting and utilising accurate data.
Data preparation requires some degree of investment in time and work, but it’s hard to get value from your data without it.
What constitutes high-quality data?
To produce high-quality data, the three attributes below should be prioritised.
Data scientists often complain of spending more than 70 percent of their time cleaning up data, but ultimately, data cannot be useful if it consists of duplicate records, empty fields, and inconsistencies in the formatting. This not only increases the likelihood of inaccurate results but also could lead to fatal business decisions that could have been easily avoided. So, don’t see the cleaning process as massaging and wrangling data but instead as enabling accurate business decisions.
It’s is also crucial that there is enough data for statistical analysis and meaningful conclusions. For example, if an organisation’s goal is to determine how marketing influences spending, if the campaigns are not being tracked in terms of whether customers are being driven to the company’s website, then the data is incomplete.
Even if data is clean and complete, businesses need to compute values to make it more useful. For instance, by grouping or highlighting frequently occurring values. In the end, most real-world data is not instantly analysis-ready, so it is okay to ask, ‘How much work will it take to get your data ready for analysis?’
How do we foster a culture around good data preparation?
No more silos
Good data preparation allows an organisation to empower not just their data scientists but also employees from all departments to use and gain actionable insights from data. This way, the power of data is not siloed and can benefit the business at all levels.
Often, the variety of sources from different departments can naturally form data siloes, as databases are often grown on different platforms. These silos can frustrate efforts to corral and analyse an
organisation’s data in its entirety.
To overcome this, team leads should work collectively to better understand each other’s roles and the collaboration needed to move towards a shared goal of delivering value to their business. Some teams can offer guidance when it comes to data modelling, while others can communicate the importance of protected data environments. This will enable business users to get the data they need when they need it, safely and efficiently.
Don’t forget about dark data
So-called ‘dark data’ refers to information assets that an organisation collects, processes and stores in the normal course of doing business. However, it eventually stops being used and does not get deleted, so it ends up being a liability rather than a benefit.
This comes down to the fact that many businesses pull data that they don’t know they have. To move forward from this, businesses ought to utilise tools and processes for moving and managing data. Better understanding their data, what’s necessary and what’s not, reduces the likelihood of dark data.
Lastly, organisations should also determine what data needs to be tracked for their data strategy and governance policies. Often large data sets are gathered without organisations considering why they are being pulled. It can easily become a compliance issue, especially if the dark data contains personally identifiable information (PII) about customers.
It’s a collective effort at the end of the day
With so much data to manage and utilise, organisations may feel as if they don’t have time to regroup and truly consider data preparation alongside a data analytics platform. However, taking a break to step back and look at the big picture will help businesses to better understand its importance.
Each and every relevant department has a part to play, so the silo walls must come down, and a move towards producing higher quality data should be embraced. Gone are the days of the burden being completely on database professionals and data scientists. A collective effort around preparing data reduces the likelihood of unwelcome surprises when the time comes to use it.