May 19, 2020

How to transform your business with embedded analytics

Tom Cahill
VP EMEA
Logi Analytics
Logi Analytics
Tom Cahill, Logi Analytics
4 min
How to transform your business with embedded analytics

It’s the new status quo for businesses: If it doesn’t get measured, you will not get budget for it. In other words, everyone – from sales and marketing to product and finance – is expected to leverage analytics to make data-driven decisions.

But just as organisations have raised their expectations, business users have also raised theirs. It’s not enough for an organisation to provide access to data analytics, then sit back and hope people use it to make decisions.

People now expect information to be available at their fingertips – whenever they want it, wherever they are. Today, when a question pops into our heads, we immediately grab our smartphones to find the answer, choosing the technologies that provide the most optimised and visually appealing experience.

The same expectations hold true for our business apps. Workers expect a tailored analytics experience in context of their workflows. When businesses provide tools that meet users’ different needs, those users will actually use the information provided to make data-driven decisions.

Why the future of analytics is embedded

Enter embedded analytics, where applications provide data-driven insights “in the moment” of every decision and every action. Embedding analytics into the applications workers use every day grants users a seamless analytics experience, because they don’t have to access multiple tools to find the data they need.

By embedding analytics deep within a single application, businesses can empower their employees to create and share dashboards, reports, and visual analytics across their organisation, with little to no support from IT. Moreover, by enabling all employees to make data-driven decisions in the applications they’re already using, businesses can minimise the time and effort that exists between finding insights and taking action.

Setting expectations

It’s important to note that all of this requires a cultural change in organisations. While embedded analytics places the data and analysis capabilities directly within a user’s workflow, it will likely take a little time for users to get comfortable making it part of their daily routines.

Just like in school, unless someone is “graded” or evaluated on something, they’re going to find it hard to dedicate time or effort to it. The first step is to not only provide users with information (charts, reports, dashboards, etc.), but to also set the expectation that this information needs to be used.

Collectively develop a few metrics that matter to the organisation, department, or group and determine the timeframes in which those metrics have relevance.  Get access to the data if you don’t have it. Begin to measure, monitor, and track individuals’ performance based on those analytics. Discuss them in meetings. This will provide focus and clarity for the organisation – and will ultimately lead to step two.

Delivering in context

As your users start to get an appetite for data, they’ll recognise that in order to succeed they need to have analytics. They will also start to realise that they can’t possibly expect a small centralised IT organisation to answer all their questions if they don’t even know what their questions are in advance. This is where self-service analytics comes in.

By embedding self-service analytics within the applications people use every day, you will enable them to answer their own questions without requesting answers from IT. Behind the scenes, you will gradually get people to change the way they think about making decisions. They will start to look for the information that can help them make better, more data-driven decisions.

Users will be able to start testing their hypotheses. If they have a hunch, they can test that hypothesis through analytics to see if it’s right or wrong. If it’s right, great. If it’s wrong, that’s also great – then the organisation won’t spend money on a tactic that wasn’t well researched.

Feeding the virtuous analytics cycle

From this point, users should be encouraged to share their findings with the wider organisation. Assume that if one employee is analysing data and generating a report with that information, then there are likely several other individuals within the organisation who would also benefit from those insights.

Sharing these insights across an organisation, at a much faster pace, will enable the business to remain more competitive.

As more organisations require their employees to report on metrics, it’s increasingly important to provide users with analytics within the applications they regularly use. An analytics culture transforms the nature of your conversations: It is one where people talk about facts and data, and demand it if it does not exist.

Meeting the demand for analytics is not simply a case of adopting the right tools – it’s about offering the right data within the context of the applications people use every day.

By Tom Cahill, VP EMEA, Logi Analytics

Read the July EURO 2016 issue of Business Review Europe magazine. 

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Jun 16, 2021

SAS: Improving the British Army’s decision making with data

SAS
British Army
3 min
Roderick Crawford, VP and Country GM, explains the important role that SAS is playing in the British Army’s digital transformation

SAS’ long-standing relationship with the British Army is built on mutual respect and grounded by a reciprocal understanding of each others’ capabilities, strengths, and weaknesses. Roderick Crawford, VP and Country GM for SAS UKI, states that the company’s thorough grasp of the defence sector makes it an ideal partner for the Army as it undergoes its own digital transformation. 

“Major General Jon Cole told us that he wanted to enable better, faster decision-making in order to improve operational efficiency,” he explains. Therefore, SAS’ task was to help the British Army realise the “significant potential” of data through the use of artificial intelligence (AI) to automate tasks and conduct complex analysis.

In 2020, the Army invested in the SAS ‘Viya platform’ as an overture to embarking on its new digital roadmap. The goal was to deliver a new way of working that enabled agility, flexibility, faster deployment, and reduced risk and cost: “SAS put a commercial framework in place to free the Army of limits in terms of their access to our tech capabilities.”

Doing so was important not just in terms of facilitating faster innovation but also, in Crawford’s words, to “connect the unconnected.” This means structuring data in a simultaneously secure and accessible manner for all skill levels, from analysts to data engineers and military commanders. The result is that analytics and decision-making that drives innovation and increases collaboration.

Crawford also highlights the importance of the SAS platform’s open nature, “General Cole was very clear that the Army wanted a way to work with other data and analytics tools such as Python. We allow them to do that, but with improved governance and faster delivery capabilities.”

SAS realises that collaboration is at the heart of a strong partnership and has been closely developing a long-term roadmap with the Army. “Although we're separate organisations, we come together to work effectively as one,” says Crawford. “Companies usually find it very easy to partner with SAS because we're a very open, honest, and people-based business by nature.”

With digital technology itself changing with great regularity, it’s safe to imagine that SAS’ own relationship with the Army will become even closer and more diverse. As SAS assists it in enhancing its operational readiness and providing its commanders with a secure view of key data points, Crawford is certain that the company will have a continually valuable role to play.

“As warfare moves into what we might call ‘the grey-zone’, the need to understand, decide, and act on complex information streams and diverse sources has never been more important. AI, computer vision and natural language processing are technologies that we hope to exploit over the next three to five years in conjunction with the Army.”

Fundamentally, data analytics is a tool for gaining valuable insights and expediting the delivery of outcomes. The goal of the two parties’ partnership, concludes Crawford, will be to reach the point where both access to data and decision-making can be performed qualitatively and in real-time.

“SAS is absolutely delighted to have this relationship with the British Army, and across the MOD. It’s a great privilege to be part of the armed forces covenant.”

 

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