It takes vision, heart and a plan to start a company. Sustaining it is not an entirely different matter, either. From recruiting, financial planning, and marketing, all elements of the business need to be taken into consideration. Each element requires its own care and attention, and yet everything has to move fast. Now.
It’s a daunting task. No joke at all.
Leveraging Data to Positive Outcomes
If we are to learn from the best, it’s evident that data is the fuel to propel your growing organization to greater heights. Google used what is termed ‘people analytics’ to develop training programs designed to cultivate core competencies and behavior similar to what it found in its high-performing managers. Starbucks practically revived itself after the great 2008 meltdown, during which it had to shut down 600 shops, by applying data analytics for its product development, pricing strategy, marketing strategy, real estate development, and more. Amazon recommends products to its digital visitors very strategically - using Machine Learning and data analytics.
So whether it be designing an internal training program, uplifting your brand from a crumbling position, or recommending the right products to prospects and customers - using data analytics, in the right way, can lead to wonderful outcomes.
The Case for Adopting Technology Early On
While Starbucks adopted data-driven decision-making after a harsh reality check, most small or medium-sized enterprises don’t need to commit the same mistake to learn. Most of us can’t even afford to.
Amazon achieving success in progressive retailing, enhancing every single online customer purchase experience, was on a grand scale. And took big bucks.
So how can an SME model its approach to these uber-successful corporations without the leisure of making mistakes and having solid financial backing?
Adopting data analytics technology early on.
The Problem - Investing in the Wrong Resources
Here’s what companies do in their initial phases: they hire a data analyst with a view to harness all internal and external data and make it resourceful for stakeholders. An analytics platform like Tableau or Google Data Studio is chosen. Requests start pouring in from the business team and eventually mount up to a level where a solitary analyst cannot possibly respond to everything. From the second to the third, over time, a team grows. A successful company can expect multiple departments to place their queries with the data team, which again overburdens the data team.
The solution this time is to pair a new data analyst with each department, so each leader can get his ad-hoc queries answered within the timeframe s/he requires.
A whole bunch of data analysts works on company data in silos for their departments. A universal set of metrics seems harder by the day, as each department creates its own set according to what they believe is efficient. Not in the long run, though.
The business requests keep growing, the data keeps getting scattered, causing holistic insights to be missed out on, and the monopoly of data still rests with the analysts, raising a whole BUNCH of other issues for both the business and the data teams.
1. What happens when the data increases? Do we hire more?
2. What about the management headache of continuous hiring and firing, since analysts are leaving due to job stagnation/ lack of job enrichment?
3. Does the career trajectory of a data analyst in your company include deep research on market opportunities and areas of impact, predictive modeling and other such strategic ventures, or will they continue to be embroiled in responding to ad-hoc queries and long mail trails?
The Solution lies in Self-service BI
Managing ad-hoc requests from business users is the first step. Self-service BI opens up the tightly controlled data environment and allows business users to enter the world of data, in a non-overwhelming manner. Of course, the pace of adoption differs from company to company, and the nature of self-service BI will differ too - a software company where people can string some SQL queries can work with an SQL-based Business Intelligence tool. The same will not however work with an apparel brand, where the data team builds entire dashboards for the business users. The latter part is where Phrazor can offer assistance, allowing business users to query the data on their own (no code), receive insights in language, supported by visualizations (with in-built high-level customization options for both - nothing too complex), plus point-and-click drill-down capabilities.
In short, lesser ad-hoc requests to deal with.
There’s no real need to list down the other derived benefits of a Self-service BI solution like Phrazor. It puts time in the hands of the data team and relieves them of incoming pressure. A healthy balance can be struck between the business and data ends of things. The overall health of the tech team improves, HR’s headache lessens. As a whole, the culture becomes one where data is prioritized.
Language is the differentiator. Communicating deep insights from data, and establishing critical inter-relationships between multiple data points is possible only through language. As basic as it sounds, it is language that serves as an effective medium for explaining charts and tables. Excel and visual-centric dashboards may look impressive, but functionality-wise, language seems to be best.
Making the Right Choice
Culturally speaking, opting for the right BI tool can decide the company’s attitude towards data, and fixate it for years to come. We recommend spending enough time on this paramount subject and making the right choice.
We would love to demonstrate Phrazor and its language capabilities to you. Are you up for a demo?