When you think about data analytics, we think of the technology necessary for analysis, like NLP, but we don’t give much thought to data preparation. Preparing data is not the most exciting function when mining big data for additional insights, but it is still a crucial step in the data analysis process.
As you may have guessed, this is the stage where data experts curate and prepare data. They have to comb through the different sources to find the relevant facts and prep them for analysis. Preparing data is one of the most important functions one can do. Without it, businesses would be working with flawed data, which compromises business findings. After all (and I am sure I have mentioned this before), you can have the most sophisticated data analytics technology, but without accurate data, you will only create tainted findings, which will compromise decision-making.
Yet despite its importance, several organisations struggle to address data preparation challenges.
Let’s take a look at some of the challenges organisations face when optimising data.
Data experts/consultants are some of the most expensive people an organisation can hire. Surely this means that corporations are going to generate the most value out of them by assigning them only high-end tasks? Wrong.
A lot of data scientists spend a good amount of their time shifting through different data sources. In fact, data experts spend as much as 60% of their time cleaning data and preparing it for analysis. It is a huge waste of resources, considering that the skills and knowledge of data analysts can generate a lot more value if they focus on more high-end work. The misallocation of talent is one of the biggest challenges an organisation faces during the data preparation stage.
Certain organisations lacking mature data analysis practices neglect context. This occurs because the IT and business departments are not in sync in their objectives. IT analysts spend several cycles curating the perfect dataset only to find that it lacks the relevant context, rendering it useless.
Incorporating context is a challenge in data preparation because it requires collaboration between different departments or business units. Resolving this particular challenge requires the mobilisation of resources and the expertise of different professionals working together towards a common goal. This is not impossible to accomplish, but it takes time to resolve because different stakeholders need to be persuaded on the value of analytics.
However, one of the biggest challenges for any organisation is to spot and fix data quality issues. Data quality is determined by the level of consistency, conformity, relevance and completeness. This challenge can be attributed to the immense data volume organisations have to process regularly. Given that most organisations are dealing with petabytes of data, spotting quality issues becomes a huge challenge – especially without the right equipment.
Fortunately, data preparation challenges are not insurmountable obstacles and as data analytics plays a bigger role in revenue generation, organisations will have to take the time and effort to address the challenges that hinder data preparation and analysis.
However, what is the best way to resolve these challenges? In my experience, there is no one single solution. Resolving data preparation challenges means investing in the right technology, mobilising the organisation’s resources, and on some occasions, a complete restructuring of operations to eliminate inefficiencies in data preparation. Through judicious planning and resource mobilisation, organisations can resolve their data preparation challenges.
Looking to resolve data preparation challenges? The technology you have will be a huge contributing factor in eliminating the challenges that plague data analysts. With the right data analytics platforms, organisations will have a much easier time resolving data preparation challenges and optimising the entire process.
Once the data collection and analysis process has been optimised, cleaning data and making sure it is accurate will be significantly easier than before. Addressing data preparation challenges leads to other benefits like more accurate data analysis. When data is clean, businesses can be assured that their reports will be accurate, which boosts confidence in decision-making.