Remove data silos in your organisation using conversational intelligence
One of the biggest challenges brands face today is managing the volume of data available to them. Particularly when that data is spread across different teams, creating data silos. This results in slow processes, reduced productivity and stunted growth. Conversational intelligence, when used correctly, can help remove data silos within organisations by creating a single source of truth that can be accessed and used by multiple teams.
Data silos are essentially separate collections of data that aren’t integrated with each other or, often, the systems a company already has in place. They are kept separate and are hard to access by other teams. In a 2019 survey conducted by ARM and Treasure Data, 54% of organisations stated that their biggest barrier to leveraging data was fragmented or siloed data, and 47% said that data was siloed and difficult to access.
So why do data silos occur, and how can companies break down these barriers using conversational intelligence?
There are a number of ways silos occur, but they generally fit within two categories. Fragmented company culture and complex technology systems.
Talkwalker Quick Search shows some of the common themes relating to data silos
In large companies in particular, it’s easier for barriers to form between teams; different teams often work on distinct business challenges. If these business areas are kept separate, then there is little need for the teams who run them to communicate with each other.
But data, in its raw form, doesn’t shed light on only one particular challenge. It can be analysed to provide insight into lots of different challenges. Two teams working on completely different projects, could both benefit from the same data. However, if the team who collects the data doesn’t share it, other areas of the business will lose out. In this case, data silos are created, not because of the complexity of the system, but because of a lack of communication with the organisation.
These sorts of barriers can be exacerbated by a culture of internal competition. Companies who encourage competition between departments, focusing on the speed of results rather than collaboration are likely to see more data silos. With no incentive to share data, instead having more reasons not to in order to come out on top, the barriers between teams get even bigger, with data becoming even less accessible to others in the organisation.
The other reason for data silos occurring is the complex technology systems in place to collect and analyse data. The issue isn’t having too little data to inform decisions, it’s having too much! And it’s often collected through different sources and stored in separate systems that don’t allow for integration of different data sources. According to a recent study by the UK fintech, Cledara, companies of 50 people will have on average 45 SaaS subscriptions, including in areas such as customer support, analytics, social media marketing, and business productivity.
Companies of 50 people will have on average 45 SaaS subscriptions
With companies using so many different tools to pull in and analyse different data sets, the majority of which are not integrated, it’s not surprising that silos occur. If the customer service team is using one tool using one data set to analyse customer feedback, whilst the marketing team is using another tool with different data to plan a new product launch, it’s unlikely that the results will be consistent and effective.
Instead, by using a tool that enables multiple data sources to be brought together in a single source of truth, brands can ensure consistency of the information used by teams, resulting in a more connected growth strategy across the business. This is how conversational intelligence helps to break down data silos.
Using a tool like Talkwalker, that can bring multiple data sources into one platform, helps create a single source of truth.
Data silos within organisations can create a whole host of challenges, all of which create barriers to success and growth. Here are the four biggest problems.
1. Integrity of data
Firstly, the integrity of the data your teams are working with is threatened. If each team is working with different data on different systems, it’s hard to keep track of what information is available, when the information becomes outdated, etc. With no overarching control or standard of how data is gathered and analysed, it’s also possible that it could get lost or be compromised by employees working on their own projects with their own tools.
2. Accuracy of results
There’s also an issue of inconsistency of results across the business. If teams are pulling in data from multiple sources, storing them on multiple databases and analysing it with multiple tools without comparing across the business, each team is likely to get different results. It becomes incredibly difficult to create a robust growth strategy if, in extreme cases, the information each team provides contradicts each other.
3. Siloed culture
Another problem caused by data silos is that, by allowing them to occur within a company, it also encourages team culture to become siloed, reducing collaboration. If each team analyses the data for their own specific purpose using their own tools, there is no need to talk to each other. A collaborative environment is important for creating a good team culture, and a place where people want to work. This in itself contributes to long term growth, as employees who advocate positively for their employer help to generate brand love.
Talkwalker Quick Search can show brand net sentiment over time. Dips in sentiment should be shared with marketing, customer service and communications teams.
4. Inefficiency of systems and processes
A siloed culture leads to one of the biggest problems caused by data silos: increased inefficiency. Teams are unable to learn from one another, and this completely independent way of working often results in duplicated work.
For example, the customer service team, when analysing customer reviews might come across information that would be relevant for the product team, such as a bug in software, or ideas for product improvements. By keeping this information to themselves, the customer service team is relying on the product team to do their own analysis of customer data to discover this same information. That requires two teams doing the same analysis: double the time, double the effort.
Many organisations have come to realise the need to break down data silos in order to improve their long-term growth. A great example of this is telecommunications company, Orange. At the start of 2019, they decided they wanted a tool that could be used by employees at all levels of the company to empower them to make data-driven decisions in their day-to-day work. With Talkwalker, they were able to combine all their different data sources with social media analytics in a single source of truth. Within a few months of the setting up the platform, over 1300 employees across 10 departments in 28 countries were using the tool, accessing the same data, to make essential business decisions.
Another reason for removing data silos, particularly when it comes to social media data, is that younger age groups frequently research products online and on social networks. As these groups age, more insights - from product ideas to brand positioning - can be gained. It’s vital for the growth and evolution of a business that all departments have access to this data.
16-24 year-olds to more online product research than any other age group.
In some ways, COVID-19 has highlighted the need to break down data silos within organisations across multiple industries. In healthcare, the need for quick and easy access to accurate and up-to-date patient information has been accelerated. Disruption of supply chains globally has also shown the importance of having a single source of truth within companies, to ensure that business doesn’t grind to a halt.
When it comes to social media data, conversational intelligence is a key way to break down information barriers between teams. As mentioned earlier, conversational intelligence brings together multiple data sources to create a single source of truth. By centralising company data within a platform that is easily accessible and user-friendly to all departments, brands can avoid many of the challenges that occur as a result of data silos.
Audit current ways of working and the tools that are used
To truly break down barriers between teams, however, it does require communication and planning. An inter-departmental team should be set up in order to make sure that all team requirements are included in the final strategy. At the initial stages of this process, you need to review the current ways of working, and identify inefficiencies in terms of tools being used and how the information is being processed. Are all the tools being used actually necessary? Is it possible to streamline the technology currently being used? Could more insights be extracted from the data than currently are, and shared with other teams? Would having more regular communication between teams highlight insights that might otherwise be missed?
Decide which data insights are relevant for the overall business needs
You then need to consider exactly what insights each team needs to gather and how these insights will feed into the overall business objective. By discussing this at an early stage, you can more clearly define the metrics that need to be tracked, so that time isn’t wasted monitoring irrelevant activities.
Define the metrics needed by each team for each business activity.
Where there is overlap in the insights required for different activities, efficiencies can be made if teams work together and share information. The outcome of these discussions should become a roadmap for improvements of sharing intelligence with different parts of the business.
Develop a standard, repeatable process to analyse data
Define future workflows and ways of working together
Once you’ve established the insights each team needs, and where there are overlaps, it’s essential to set up a new workflow that will ensure data silos don’t emerge again. This should be agreed between all teams, and a benchmark should be set, along with KPIs that can be tracked over the following months to see whether efficiencies have been made and progress towards the collective business objective is being achieved.
Monitor the new way of working and look to see if more improvements can be made
Continue to track progress of the new workflow now that data silos have been removed, and see what new information can be gained through using conversational intelligence. For example:
Benchmarking brand sentiment. Understand how your audience feels about your brand, your products or different topics, and share this information with the customer service, product and content marketing teams respectively.
Improving social customer service and reputation management. If there is a spike in brand mentions with negative sentiment, it’s a signal to share this information with the communications team, to prevent problems from turning into a PR crisis.
Improving campaign effectiveness. Define metrics to monitor through conversational intelligence to assess the performance and ROI of your campaigns. If you’re launching a new product, you might want to track engagement with your posts and the sentiment of mentions. You can also track this against business impact metrics, such as increase in sales during the period of the campaign. Using a tool that offers real-time insight, means you can continuously track the campaign and tweak different aspects as necessary to make sure you achieve your ultimate goal.
Informing product development. By analysing audience opinions on different products, industry trends and learning about their pain points with your product or service area will uncover new product ideas or ways of improving your existing products. Tracking your competitors and consumers’ opinions of them will also give you a better idea of where to focus your efforts.
Breaking down data silos within your company can create efficiencies and a better work culture that will help improve productivity, ultimately leading to growth. Integrating conversational intelligence into your strategy will help to break down existing barriers, but to truly benefit from it, you need to consider a few things:
Maturity: what are your current capabilities?
Goals and strategy: what value can you deliver through improved social media listening? Which priorities should be on your transformation roadmap?
Evaluation and reporting: Do you have the right listening and reporting tools?
Data integration: How can you improve sharing of actionable insights?
Process: How can existing processes to monitor, review and action be improved?
Once you can answer these questions, you can begin to develop a company-wide strategy that will help you achieve your growth objectives and transform your brand.