Traditionally, building and implementing a data strategy was the exclusive remit of senior IT analysts and boardroom-level executives. Information was gathered and collated by a small team, and used to drive decision-making from the top down.

Now, however, individuals are becoming empowered by improved data collection and analytical functions at a company-wide level. Data is no longer just a boardroom-level consideration: it drives strategic and tactical decision-making at every rung of the corporate ladder.

>See also: 5 ways to improve a data strategy

But as it grows in importance, the challenges inherent to handling vast quantities of information grow in kind. Understanding and overcoming these challenges remains essential to building a business-relevant data strategy. Here are four essential rules to follow.

Define clear business objectives

Too many businesses approach data as some kind of miracle cure-all that possesses every answer to every question. This is not the case. To succeed, you need a clear strategy, clear vision, and clear goals; data will not give you the answers if you have not first determined the questions.

So, if, for example, you’re running a recruitment firm and you want to know how to improve the number of candidates you can interview or place, start by asking the obvious questions: “Where are we efficient and inefficient?” “What are we doing poorly that our competitors are doing well?”, and “How do we become more profitable?” You should only start shooting when you know what you’re aiming at.

Once you’ve determined the “What?” and “Why?” of your data strategy, it’s essential to consider the “Who?”. The answer, in most scenarios, should be “Everyone”.

According to a report from EY, 81 percent of organisations support the notion that data should be at the heart of everything they do.

>See also: Operating a successful data management strategy

Therefore, to build a truly data-driven organisation, you must have support for your strategy from staff at all levels – from the boardroom to the front desk. A good way to facilitate this is to establish uniform, companywide processes for collecting, storing, and sharing data.

Accordingly, make sure those who need to be on-boarded to using new technologies are treated as a priority, and that any concerns they may have are discussed openly and dealt with swiftly.

Avoid data silos

Data is only useful if everyone who needs it can access it. When it’s siloed in different systems across different departments, it can’t perform its most basic functions.

In fact, according to a survey from Crowdflower, 80% of the work involved in data science is acquiring and preparing data. If accessing data is hard work for data scientists, you can only imagine the difficulties your employees can face every day.

True, there are instances in which you cannot readily share information with another department, even when it might be helpful: a customer’s card details, for example, should remain safely ensconced within the records available to the finance team – accessible to no one else. However, if there are no such legal obstacles, it’s worth being as transparent as you can be about customer information.

>See also: Data is driving new skill requirements

A shared IT infrastructure is essential. Staff who use the same systems and data sets work more efficiently, and are less likely to work from conflicting information.

For example, customer support can share the history of a lapsed client with the sales team that’s trying to win them back, while IT can share data about site activity with the marketing team that’s trying to develop a digital strategy.

Isolating your data is never the best way to get the most out of it. Be transparent with your information to the extent that you can. Sharing is caring.

Don’t rely on data warehouses

Many companies assume that more data is better, and they tend to overcompensate as a result. Simply put, the more data you collect, the more likely it is to be irrelevant and misleading.

Indeed, many businesses build huge, costly data warehouses and then leave them there, doing precious little with them, making them accessible to no one save the data analysts.

>See also: The UK’s top 50 data leaders 2017 

The information isn’t leveraged into applications that customer-facing employees can operate, and is often a mixture of useful and useless details. Instead of investing in these large, generic warehouses, focus on business-relevant databases that can be accessed through CRM and business intelligence systems.

These tools do not require an in-depth understanding of data analysis, and they can help staff at every level start using this information to drive efficiency and productivity.

Don’t use your data as a crutch

Data isn’t a crutch, and automation isn’t likely to make all human employees completely obsolete. The person-to-person interactions that support customer service, care, and support remain crucial, and a machine cannot yet accurately simulate them.

Building stronger relationships is, however, something that technology can help with, but ultimately most customers don’t wish to forge a long-term bond with a machine. What they want is to feel like your only priority – not a lone entry in a vast database.

>See also: The value of data driving business innovation and acceleration

Data-driven technology should be used to make them feel important, not anonymous. Use it wisely, but don’t use it as a replacement for a personal touch. At its best, data augments and supplements human effort: it is no substitute for it. It can tell you when to make a call, but it can’t help you charm your customer into engaging with your brand.

Sourced by Peter Linas, international managing director, Bullhorn

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