Decision-making with automated dashboards and real-time KPI reporting

Once we confront the task of elaborating reports, automating data collection and representing them in a dynamic and efficient form, we’ll be able to focus on qualitative analysis and drawing valuable conclusions that will help the organization’s tools and tactics. To do so, we should choose the right tool with the aim of creating our automated dashboards. In a past post, we gave our recommendations for what you should consider when correctly choosing your tool. 

What would be the next step? Make some decisions about different software to automatically generate real-time KPI reports. The flow will have the following steps:

1. Decide between transparency or privacy

Do we want to make certain data visible? It will be useful for gaining and brand awareness and perception by posting precise details that highlight corporate culture. Good public dashboard examples are Buffer, or those the public images that Klipfolio shows.

When it comes to getting people involved, sharing, and opening information streams between departments we can keep certain results private but disclose them to the company to keep them informed or to show the rewards we reap from them. Why leave old documents in circulation when you can have an updated representation of what happens in every area?

Lastly, there will also be dashboards only visible for certain teams or shared with vendors, clients, or decision-makers.

2. Pick the appropriate metrics

It’s clearly not the same to start making dashboards for an area, platform, or other division (the web, social media, SEO, CRM, customer service, finance, management, etc.). Each one will have their metrics they’ll need to track and analyze, so we’ll have to suggest contenders for what they should include and, most importantly, which will be the key indicators of performance or KPIs. This image depicts an example of the variety of available metrics:

Before we get there, we must do background work of course: clarifying what objectives we’re going to define. By combining indicators or KPIs that are going to be the accepted and proposed criteria, we’ll have the quantitative targets to verify whether standards are progressing or achieved.

3. Play with the data

We’ll compile the data sources that will nurture our views. Ideally, they’ll integrate through a database or an API. Sometimes, you’ll have to connect them with an outside service that authenticates data, and there will be others that leave you with no choice but to integrate the data with a spreadsheet in the cloud where we’ll update those pieces of data we couldn’t directly hook up. 

Later, we’ll play with the gathered data through these sources by mixing them, grouping them, cleaning them up, or formatting some aspects like dates or text. It’s optimal that the tool you choose has similar functions, or that the SQL searches are accurate enough. 

4. Visualize the information

The time has come to connect the viewing and data: first, we’ll present the sketches that give us an idea of the layout of a first version. We’ll pick a representation that best fits the type of information we’re seeking to show: evolution, a quote, dispersion, correlation, a median example, etc.

It will be crucial to have drop-downs, the ability to filter, order, download the data to give a good experience for all the teams that use the dashboards. We’ll always have the corporate identity present from the color scheme, shapes, logos, or font, so we always have the most coherent manifestation in all the reports and presentations coming from the organization.

5. Take care of communication

The relationship between teams is a critical part of the entire process. An agile methodology to receive feedback, modify, and show it again is vital for developing the project. Orderly communication will help us group together all these details for improvement: changing the graphics, colors, sizes, structure, layout, or design.

All this work requires coordination.  With that in mind, it’ll be useful to have a project management tool like Taiga or Trello to order to-do lists and monitor progress, react in time, and get the next version as soon as possible, and closer to perfect every time.

6. Review and repeat

Once we check that the automation works (and if that’s not the case, find out where the error’s coming from and rectify it), what’s left for us to do is draw our first conclusions. Here we’ll transmit the primary value from the entire process, with the aim of providing relevant information that improves decision making in the organization and positively impacts profitability and the happiness of the people in the firm. 

After that, we repeat the process and make the next conclusions (we can take a well-deserved refresh between points in time).

7. Try new things

Once everything’s going off without a hitch and enough time has passed, we can start thinking about trying new things. For example, if we have a feeling and want to validate it, check that we’re right, or maybe disprove it and end it. To do that, we’ll make a change in the area and isolate the key variable or conduct A/B testing to observe what happens based on our dashboard. Did the expected correlation or variation occur? We’ll be able to continue this route or eliminate the possibility and run more tests.

We’ll be ready for improving our organization with data-driven decisions!