search.noResults

search.searching

dataCollection.invalidEmail
note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
Data analytics: building a data-driven culture


To use data effectively as part of an evidence-based practice approach to decision-making can require organisational cultural change. Dr Sue Shortland explains.


O


ne of the main obstacles to instituting evidence-based practice can lie in implementing data findings. Hence, even with a critical appreciation of data, HR and global mobility professionals face communications


and cultural challenges. Explanations are needed of how people analytics can contribute to business outcomes and how additional value can be derived from such data and scaled up across the business. This means that emphasis has to be placed on embedding a data-driven culture and communicating its benefits.


DEVELOPING DATA AWARENESS To develop data awareness, it is necessary to provide explicit communications and feedback to nudge people towards desired behaviours. To ensure that data is appreciated and used to best effect within organisations, interventions that facilitate its use are needed. For example, HR and global mobility professionals must present data in a user-friendly way, with a focus on its contribution toward decision-making. Emphasis on the decisions that people need to take is required, otherwise data can appear bewildering and superfluous. Not everyone is data literate and so anyone who will be using data


must be properly trained, so they can achieve a good comfort level in their understanding of what data tells people. This does not mean that data users must become data manipulator experts; specialists are likely to be employed to analyse, clean and present data for more general use. Rather, HR and global mobility professionals need to be trained to ‘read the messages’ within data and to tell a clear story in comprehensible language as to what these messages mean aligned to the needs of specific audiences.


CREATING A DATA CULTURE To create a cultural shift where data can underpin decision-making, a change in the physical environment can prove exceptionally helpful. People tend to fall back on what they have used before to make decisions – particularly if these systems are readily accessible. Thus, old systems that no longer support new ways of working and can stand in the way of the adoption of new tools and techniques should be replaced. Interactions can be improved through the redesign of workspaces,


PEOPLE


ANALYTICS See page 26


aiding the communication and collaboration necessary to discuss people analytics data and facilitate group decision processes. To ensure that data remains at the heart of decision-making, reward systems linked to data-driven key performance indicators (KPIs) can help to embed evidence-based practice outcomes. Top executive commitment is also required to change and embed culture to become more data-driven. It is crucial to remember that data should not replace human


judgement – managers can see data as intimidating, undermining their role in decision-making and threatening their jobs. This fear is far from the case, as data analytics can augment managerial decision- making, but this perspective needs to be communicated and embraced. The benefits of using data must be explained in terms of enhancing people’s capabilities to do their job more effectively. In essence, data analytics can give sound insights and credibility to HR and global mobility professionals in their discussions upwards with the board and in reaching out to the line, providing insights that demonstrate an understanding of business challenges. A data-driven culture should not be feared, rather considered


as a tool to improve credibility. For example, gaining the ability to speak the language of business through the use of data can assist in partnering with finance functions, while using data to demonstrate ratios and dynamics in diversity and inclusion can help project forward to make meaningful change.


DATA INTEGRITY Data quality is critical to sensible and effective decision-making. Up-to-date and complete information is needed. The expression of ‘rubbish in-rubbish out’ should always be held up as a warning within data-driven cultures. HR and global mobility professionals need to ensure that those who supply data (such as managers and employees across the business worldwide) input good data. To encourage this, non-negotiable KPIs for which good-quality data is required could be prioritised. In addition, to ensure that people across the business at all levels see the value in supplying good-quality data, ‘data champions’ might be used who can influence, encourage and motivate others to see value in and enjoy the meaning of data. People analytics is a growing field and is likely to underpin


business decision-making to an ever-greater extent in the future. Building a culture within the business that emphasises the value of data in evidence-based practice will become an increasingly crucial element in the working lives of HR and global mobility professionals.


For more information on critical interpretation of data for improved decision-making and evidence-based practice, visit bit.ly/evidence-based-practice


Enter the Relocate Awards and showcase your analytics expertise.


2020 THINK


RELOCATEGLOBAL.COM | 21


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56