5 Capabilities You Need for Agile Analytics

Successful companies are prone to dynamic environments and with these come dynamic needs.  So when on the lookout for new analytics platforms the first thing that comes to mind is agility.  Why is agility such an important factor when looking to buy big data analytics? Well, firstly adding a new tool takes time, from the planning to the execution (we’ve all been there…) and secondly it’s costly, very costly.

Here are the top 5 must have capabilities when building or buying agile analytics:

 

1. Querying Complex Questions

Complex questions cannot be answered by simple dashboards. In the beginning it’s all great and easy, you’ve got your basic metrics and there’s really no need for this so called agility.  But what happens when you start collecting millions and billions of data points a month? You also start collecting different behavioral segments, drop off points and the need for more in-depth analysis arises.  This is when it first hits you that you need a dynamic agile system. If you don’t have one of these you’re going to be on the lookout for a new system or you might be ambitious enough to try and build your own solution.  But there’s really no need for that.   Today’s agile analytics systems enable you to write in-depth queries with the help of more complex dashboards.  For example having out of the box widgets are great for your basic KPI’s but when you’re going to want to start tracking user behavior that when you’re going to need free hand SQL, cohorts, paths and funnels.

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2. Unified Analytics

Data is coming in from lots of different sources, most people would call that a problem, we don’t think that at all, on the contrary that’s great if you’ve got the right tools to handle it. Analyzing behavioral data alone is not good enough anymore. Data is collected from a variety of sources and organizations manage data on several databases simultaneously. One system collects behavioral data, a second collects campaign data, a third may store financial interactions and so on.  Looking solely at event data does not tell the full picture any more.  Unification enables you to have the freedom to really understand what your users are doing all over your different channels.   But not just your basic sources, one of the main problems analysts come across down the road is unifying operational data with event based data.  For example you’re going to want to measure the affect your marketing campaign had on your users behavior.

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3. Open to Third Party Tools

Because we are prone to dynamic environments we’re going to want to have the full flexibility to easily integrate third party tools instead of being tied to one UI tool. What if you could easily integrate any third party tool into your analytics platform? The options are endless and that’s when you can really customize your analytics to fit your business needs as they happen.  Today’s high end analytics tools don’t limit you to their structure, they are open and flexible.

 

4. Having Full Data Warehouse Capabilities

Over the past few years, data warehousing capabilities have tremendously evolved to meeting enterprise standards, addressing different cases such as velocity, variety and volume. Some of the basic capabilities you’re going to need when looking at different solutions are:

  • Querying – Your data warehouse need to be capable of dealing with repetitive queries. Repetitive queries support dashboards and reporting requirements when addressing a large amount of visitors.
  • Scale – This applies to multiple data structures and formats. You need a data warehouse that can deal with large amounts of data in order to address the management of query workloads and query optimization.
  • Real-time loading – In today’s world bulk and batch loading remain the most common method. More advanced data warehouse technologies are moving to continuous loading methods, which means that data is being loading from operational sources in real-time.   This enables you to ingest stream data and perform updates for read optimization.

 

5. Personalized Portals

You’re going to want your chosen solution to have a UI SDK in order to personalize it to match your own needs. For example building an affiliate portal by using embedded widgets to personalize UI for your own needs.

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The Conclusion

The main question all BI analysts ask themselves is how to keep their solution future proof forever.  We build our system for a specific purpose and then wonder why it’s not agile.  You won’t see a return on your big data platform if you only use it for one need.   Choosing your big data analytics solution is a challenge on its own, before and after.  One things for sure, agility is key to a successful, future proof decision.

 

 

 

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1 thought on “5 Capabilities You Need for Agile Analytics

  1. Another capability could be – Process and tools to separate engineering vs Data science in analytics. Engineering – Where IT manages the environments, data, integration, warehouse functions etc – Data Science – in which business plays and create the information. Most of the time business spends time in identifying the right data from big data – like data integration, quality, cleans and less time in mining the insights. And most of the data management tasks can be isolated and industrialised through engineering services –like data lakes, buriness rule environment, EII , Data flow lineage ( not traditional lineage- it is movement of data through various process /integration areas) – then business can continually build up on insights.

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