Data....there's more and more of it, and using it intelligently can propel your business to new levels.
Most businesses, large and small, now create extraordinary volumes of data from their core systems: online retail, financial transactional and customer feedback (including their inbound and outbound emails) to name just three common examples. At the same time, the market has seen the introduction of data analytics tools that can leverage huge data stores in ever-more complex but useful ways.
Regardless of the size of your business, if you're not leveraging your existing data assets to propel your business to new heights, you're behind the times. Fortune 500 companies have seen their investment in data science increase exponentially, and you can be sure your competitors - large and small - are exploiting the benefits provided by sophisticated data insights and analytics (aka "Data Science") to inform and determine their strategic direction, and set the stage for their growth.
Leveraging 'big' data with data science - what's different to the way we used to manage data reporting?
The old paradigm for managing data was restricted by the historic tools and technologies of the time. "What's the total of..." and "Compare the averages..." were legitimate questions to ask of relational databases (which are still the most common kind) using traditional data analytics tools. While these databases are easily amenable to summarisation and aggregated reporting, the reporting tools themselves usually lacked any ability to predict, model or forecast. In contrast, the "Data Science" approach answers questions such as "Where can we expect to be?" and "What will happen if..." we make certain decisions - far more useful questions for your business.
You might have ascertained that adopting a "Data Science" approach is qualitatively different to the old way of analysing data. It is indeed, and requires a very different skillset too - one not usually available in-house using in-house capabilities. The skills required to extract the value of these data assets to their full potential using the tools and processes that are part of the newer data science paradigm are not 'run-of-the-mill', or even things that a typically support-based IT department is staffed to do, or develop for that matter. You need professional, data science-focussed help to turn these incredible data assets into actionable information that you can use to improve your bottom line.
But what is "data science" anyway, and how is it different to data analysis?
The easiest way to understand Data Science and the ways it is different to data analysis is to understand its historic progression from earlier efforts to analyse data:
|Analysis stage||Description||What you get|
|Business/Data analysis||Extraction of data from transactional (eg. sales) data stores, and use of simple statistics like averages, variance and distributions to examine historic performance||Data summarisation of historic information|
|Data Insights||Trend analysis over time, basic forecasting using existing models only||Identification of useful information from data stores eg. simple forecasting from time-series analysis|
|Data Science||Predictive Analytics ie. model development using advanced statistical and forecasting techniques (learning algorithms eg. genetic/machine, unstructured textual analysis for sentiment identification)||Model-based forecasting of future states ie. prediction, including behavioural forecasting|
At Blad we can help...
We've long been using data insights to assist businesses grow and prosper, and can provide short-term contract work to solve your problems today. We can process data in almost any format (including unstructured or textual data), provide efficient, automated data cleansing services, and can develop hypothesis-based modelling to help you test and confirm your own insights using objective data. Using today's tools, the process is quick, efficient, and most importantly, cost-effective. You can answer a single question or hunch, or adopt a 'dig for gold' approach to identify opportunities you might not have seen coming. The result is the same however: actionable insights that propel your business to new levels ™.
How do we do it?
We use the latest technology to perform predictive modelling to create data insights, and we have the formal mathematical, statistical and commercial background that's required to use these tools effectively.
What's a typical job look like?
We usually start with an 'information audit' that reviews your available data assets and determines those assets that are going to be most useful for modelling your business. Next, we analyse the structure of your data assets to identify those components we feel are most important for the subsequent modelling step. We then involve you in discussions where we create the hypotheses that we agree are of value to investigate, given the data you have available, and your own insights. Next we develop a series of models that we jointly believe will best model your business now and into the future, consistent with your strategic objectives. Finally, we run the models themselves, asking ourselves "what would happen if?..." and present these to you as options (with outcomes) representing the trajectories you can expect your business to follow given the recommended actions.
What's going on (technically)?
For those technically minded, most jobs start with a data inventory, then an extraction and 'cleansing' step achieved through the use of Python scripts in conjunction with the PANDAS library. Once your data has been converted into a readily 'analysable' format, the real predictive insights work can begin. For this step, we use industry-standard RapidMiner software. With stunning accolades, RapidMiner excels at creating, testing and implementing insight-based modelling using a variety of sophisticated technical tools that can involve machine learning, genetic algorithms and decision tree analysis. The effective use of these tools requires formal training and qualifications in their use - and we have both of those in spades based on years of experience in commercial settings across a variety of industries.
The next step...
Getting to know Data Insights and Predictive Analytics.
If you're interested in seeing a worked example of data insights and predictive analytics in action, check out this short article.