The amount of data businesses possess is growing exponentially. With more potential sources of information than ever, it's becoming increasingly common for firms to collect as much data as they can, before worrying about how to use it later.
However, a key question for many firms then becomes how to effectively store, process and analyze this information quickly and cost-effectively to gain the best results. Traditional solutions for data storage, such as siloed structures, are often unable to cope with the volume and variety of data today's businesses own or keep up with the speed expected.
For many firms, the answer is a data lake. This consists of a single repository into which data of various types and structures can be stored in their native format until needed for analysis. Unlike a data warehouse, which has a predetermined structure and hierarchy for its contents, a data lake is a flat architecture where everything is treated equally.
This offers a range of benefits. Data lakes offer great agility as they are more accessible and can be reconfigured as needed at any time. They’re also cheap to implement and manage.
However, if you're not careful, it can be easy for a data lake to get out of control. This can happen when the data isn't managed properly and becomes hard to find or extract effectively.
Signs your data lake has become a swamp
If this happens, your data lake can easily turn into a swamp. In this case, instead of clean waters you can dip into to analyze data easily, you'll end up with an oversaturated mess clogged up with unusable or irrelevant information.
Sign that your lake is turning into a swamp may include:
- You lack metadata: Metadata is vital in providing vital context to data, such as its source. If you don't have this, people may find it impossible to find the information they're looking for.
- Irrelevant data: Many firms now simply harvest as much data as possible and dump it into their data lake, on the assumption it might prove useful later. This isn’t often the case, and the more useless info you have, the harder it’ll be to separate the useful data from the rest.
- No governance processes: Data governance sets out who's responsible for data, how it should be handled and what limits should be placed on it. Without these rules, you could find vital data falls through the cracks, or leaves you open to regulatory actions.
- Dependence on manual processes: If your employees are still managing data lakes by hand, they can quickly spiral out of control as volumes become too big for IT pros to cope with.
These may be more common problems than you realize. In fact, Gartner estimates as many as 90% of data lakes will become unusable as they become overwhelmed with information. So how can you address these issues and return your data lake to the clear, usable resource it's intended to be?
5 steps to clean up your data
There are a range of steps you can take to improve the quality of these solutions, but essentially, it requires you to be more focused about the data you input into the lake, have well-defined plans for managing this information and embrace some of the latest technologies to reduce the burden on your staff.
Here are five key elements that should be a part of any successful data lake strategy.
1. Set clear goals
A crucial first step should be identifying clear goals for your data strategy. This is vital in stopping you hoarding irrelevant data that does nothing other than gum up the works. Instead, set out what you intend to achieve and then ask yourself what types of information you'll need to help with this. Don't just gather everything 'just in case'.
2. Unify your data sources
A key feature of a data lake is its ability to collate data from a wide range of sources across many structured and unstructured formats. But this can result in data being loaded multiple times from various different places, sometimes with incomplete information or the same data duplicated across different formats. Avoid this by uniting and reconciling your data pipelines with an effective data management platform.
3. Catalogue your data at entry
Ensuring data is fully tagged at the point of entry is vital in keeping your data lake searchable. Make sure that the metadata you add is complete and consistent across every piece of data, describing exactly what it is, where it's from and what it's about. Without this, many useful pieces of data may disappear into the lake, never to be seen again.
4. Implement strong data governance processes
A clear data governance policy is another must-have. This should detail who has ownership of the data, who has permission to access it and what your processes for protecting and minimizing the data are. For example, this should include information about when data should be considered 'expired', which ensures you're not only removing old data when it's no longer useful, but also maintaining compliance with privacy regulations.
5. Use automation
Automation technology has come a long way in recent years, and embracing this can make a data lake operate much more smoothly. Whether it's relatively simple tools, such as robotic process automation for data entry to complex artificial intelligence and machine learning tools, this can help you ensure data is loaded correctly and then sort through it to identify the right data to query much more quickly and accurately than a human would be able to achieve.