Considerations For Successful Self-Service BI And Analytics

In the data-driven age of 2018, more people within businesses find it necessary to gain insight from their data– to remain competitive in the market, but also to meet constantly fluctuating needs of employees within their companies.

As a result, leaders in business are looking at the power of self-service BI and analytics in order to help them overcome such challenges. However– this new working method does not come without challenges of it’s own. If you want to find out more about leveraging self-service analytics to your advantage and scaling up in your organisation, consider these 5 factors to help you make the best decisions for maximum efficiency output:

No. 1) Security is King

Implement secure practices from the start to avoid compromising your data.

Data breaches and hacking are becoming more and more prevalent in the media where data-reliant organisations are concerned. It is not uncommon to hear on the news of companies being hacked for their their customers’ credit card information, amongst other personal data. Of course, this can be the death of your business, so taking steps toward preventing it ever happening is key. Despite the fact that more analytics do not involve personal consumer information, you should consider the importance of the proprietary business information that you’d not want discovered, especially by competitors.

So begin your transition to self-service analytics with security first. Lead by developing security standards to avoid mistakes in the future that could cost you your business. The simplest security control that you can implement right now, is to password protect sensitive reports or analytical studies, which cancan often be accomplished with the reporting tool.

If you have more comprehensive tools and capabilities, you can be more specific where user security is concerned; you can get down to which data elements a particular user can access et cetera. The way to protect these more advanced capabilities, is to encrypt your data– especially if it is in the cloud. If you’re sending emails outside of your company, a simple encryption program can secure data assets against many hackers who will be looking in places that you least expect.

No. 2) Accountability

Create a verified process for report certification, in order to engender cultural trust in the analytics.

The more reports that are generated by different teams, the more they are consumed by broader audiences. The knock-on effect of this, is that the standards and practices leveraged by each of these different teams will inevitably vary. For example, one team may not use a source of trusted data. On the other hand, another team may not use the company’s data dictionary to create their metrics. As you can see, there is plenty of room for variation which can lead to inconsistencies. The more inconsistencies there are in the reporting, the less trust can be built amongst consumers. This is why creating a verified process for report certification can help align standards and aid a consumer in understanding the level of careful examination and effort that went in to the end product.

No. 3) Don’t skimp on quality.

Enforce good data quality practices, as they will help create trusted data and faster analytics.

Data quality is paramount if you want a strong foundation for your analytics process, just like every building needs a strong, underlying infrastructure to hold it up. If you are certain that your data is of high quality and therefore can be trusted, streamlining your analytics process will be easier than ever before. Make sure that your dataand meets the criteria of data quality standards which enables all your analysts to work on the analysis itself! High quality data means that your analysts will not have to waste time scrubbing data sets– and avoid them not being able to use certain data due to the lack of data validity in the worst case scenario.

Make sure to check for the following in order to ensure your data is meeting quality standards:

Completeness of the data, or checking for any missing or partial data in the data set.

Consistency requires that the values of the data must be consistent throughout the data set.

Checking for data duplication: removing or correcting records in a database that are exact or partial duplicates of each other.

Conformity, or how well the data adheres to standards

Accuracy, in terms of the data validity and how many errors are in the data set.

Integrity of data: addressing the accuracy and consistency of the data over its life cycle, ensuring that when the data moves from system to system it maintains a level of quality and standardization

No. 4) Standardized guidelines promote consistency.

This will ensure team effectiveness to compliment the quality of your data.

When a new business begins the journey into self-service analytics, it is commonplace for teams and analysts to develop a lot of one-off metrics, data definitions, and reporting user interfaces (UI). To create consistency and ease of use in the future, make sure that you’re developing a set of standards that all teams and analysts adheres to, so that the data can be properly manipulated and leveraged later on– across the entire organisation. It is only this way that will block risk of data duplication and overlaps. An example of this would be two different analysts carrying out the same function, but naming it a different process. The end product would be a repost with two different definitions for the same outcome– which will engender lack of trust between analysts, but also for the consumer.

Standards are created to avoid such issues. Data definitions and lineage standards can be stored in a ‘dictionary’ which is open for all analysts and teams to see. Allowing teams to add their own definitions still promotes independent work and doesn’t make your teams feel like they’re following one formula. Each definition added to this ‘dictionary’ should include information about where each data element was sourced, any transformations that were applied and other relevant information for the future use of others. UI/UX standards refer to the look and feel of the reports that your analysts develop, and how users receive them. Making this look and feel more standardised will help users quickly interpret multiple reports rather than having to relearn the UI for each new report. Overall, your functions will be quicker and your results of a higher quality.

No.5) The time of Data Science is now!

Make sure to embed data science in your reporting solutions for a better result.

A lot of business analytics teams have a need to employ either a full-time or part-time data scientist– who usually is not a core component of their existing team. This person’s skills are the gateway to your team members ability to generate deeper insights from the data, which help in making better business decisions. The issue lies in the fact that they are an ‘add-on’ for an existing team: a lack of consistency means that the insights generated from the data scientist in questions, are usually a one-time study or only repeated on request (on an ‘as-and-when’ basis). If you do not have a full time data scientist on hand, you should engender your team to continually use these valuable insights where possible in a consistent fashion. These insights can in fact be leveraged by your team’s analytics solutions, enabling consistently better decisions for a broader audience!

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