In my last piece, I discussed how to support the use of data by different teams across an organisation and what steps you should take to make this work. In this piece, I’d like to tackle an issue that is related to this: use of data across companies with operations in multiple countries.
Now, not every company works across borders. Many departments adopt a centralized model for IT and can provide reports from a team of analysts that responds to requests. However, as companies scale up their operations – or as they have multiple companies that operate under a specific brand – this model becomes less satisfactory.
The first reason for this is time. If you are working across various time zones, then each round trip between the requester and the analyst will suck up more time. For businesses that work in markets where time is a factor in performance, such as Fast-Moving Consumer Goods, this additional time is no longer acceptable. Users want information to help their decision-making, and they want it now.
The second reason is how reporting is used. Each country or region may report its sales or financial performance data to meet its own local requirements. However, this may not be useful to the central sales or finance team that has to compare these results against other businesses in the same region. At the same time, additional time and people resources will have to be deployed to make this data fit for purpose centrally, too.
Solving these problems can be difficult. Country teams may want to make use of their own data visualisation products to help their decision-making; indeed, they may already have invested in tools for this reason. Multiple different Cloud tools can be in place, making it more difficult to compare visualisations. Data can also be held in different sales, marketing or ERP systems, depending on how the company has evolved over time.
Making decisions locally with centralised data
The first step to solving this should involve looking at which data should be centralized and which data will remain on the “edges” of the organisation and used by local – or decentralized – teams and individuals. This normally ends up being a decision where local teams can have their data and work with it, versus what data is held centrally and then provided out to the local teams. The challenge is that all this data still has to be combined for it to be used for analysis and decision-making.
Instead, it’s worth looking at how to split the centralization and governance side of data analytics away from the visualization and local use section. This involves looking at where the various data sources reside and how to network all the data together while still being made available for individuals within different countries to use.
There are several steps that companies can take to improve governance over their analytics in these geographically dispersed environments. For central IT groups to support their local countries or regional business teams, here is a guide to getting started:
Step 1 – Understand who is involved and what they need
To start with, it is worth carrying out an audit of the business and how things are currently working. This can identify where business teams are crying out for support, where teams have invested in their own tools locally, and where individuals are spending their own time and resources to build spreadsheets or reporting tools that meet their needs.
This can also identify where there are other dependencies within reporting. For example, a regional marketing team may provide reports that are then used by individuals within the countries covered by the region.
Alongside this, it is worth looking at the business processes that these region or country teams are responsible for, and whether they are the same for all the teams involved. Some marketers may have full budget control and attribution of their spending to sales performance, while others may only need reporting on channel attribution, for example.
Step 2 – Look at where centrally governed data can be provided for local use
Rather than simply providing reports and managing the Business Intelligence or analytics function centrally, the aim should be to provide these tools out to the individuals or teams involved. The reason for this is simple: it’s easier for people to meet their own needs and requirements for analytics than it is to describe that requirement to someone else. Similarly, the value of experimentation with analytics should not be discounted either.
For this reason, it’s important to look at centralisation of data for governance as separate to the use of that data. The role of control here is to ensure that all data being used for analytics is accurate and timely for the person using it. This includes taking local reporting criteria into account. For example, rules on recognising revenue may be different for the company as a whole, compared to how the country team measures its sales performance.
Networking data sources together is one approach that can help maintain control and governance while also making the use of that data easier. By bringing data together into a virtualised instance, each user can carry out his or her own analytics with that data without physically moving, changing or otherwise impacting the raw data itself. Similarly, central financial reporting can be carried out using the data centrally, while this data can also be provided back to the local team in its format for both financial and operational reporting.
Step 3 – Letting local data get mixed with central data
Once you have centralized governance of data sources for teams, it’s time to work on how local or individual data sources can be included, as well. For example, marketing teams in one country may use a data supplier in their sector to enrich their data, while other countries may use other suppliers or different sets of information to reach the same result.
Centrally integrating and managing this data is not a practical approach. If the data changes regularly, then this can slow down the process considerably and reduce the opportunity for local teams to get value. Instead, it should be possible for local teams to add their data into the network and then include it in their own analysis phases.
Over time, local data requirements can be monitored to ensure that analysis and decision-making by each team is accurate and no mistakes are being made in how data is used. Providing this kind of advice and guidance centrally can be useful for teams where decision-making is increasingly being guided by insights from data. Making sure that people are getting data in the right formats, in a timely manner, can be an opportunity to share best practices and keep users on the right track.
Networking data sources together can help multi-national companies support their teams with better access to analytics. Rather than relying on local teams using limited tools to work with data in silos, enterprises can and should look at how to govern data centrally while empowering local teams to augment the decision-making process with their own data.
This requires understanding of how each country or region team will make use of data, as well as the management approach to balance both central and local needs. Networking BI instances together can help meet the needs of all these different stakeholders.
The preceding article was originally published in It Pro Portal on May 13, 2016.